{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12f29c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aa969264",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "\n",
    "\n",
    "# Define the LSTM model\n",
    "class LSTM(nn.Module):\n",
    "    def __init__(self, input_size, hidden_size, output_size):\n",
    "        super(LSTM, self).__init__()\n",
    "\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)\n",
    "        self.fc = nn.Linear(hidden_size, output_size)\n",
    "\n",
    "    def forward(self, x, hidden):\n",
    "        output, hidden = self.lstm(x, hidden)\n",
    "        output = self.fc(output)\n",
    "        return output, hidden\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4202b25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7efd5802f790>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0f551f19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 201)\n",
      "(1, 101)\n",
      "(101, 201)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the .mat file\n",
    "mat_data = scipy.io.loadmat('Allen_Cahn.mat')\n",
    "\n",
    "# Following is the code to plot the data u vs x and t. u is 256*100\n",
    "# matrix. Use first 75 columns for training and 25 for testing :)\n",
    "\n",
    "# Access the variables stored in the .mat file\n",
    "# The variable names in the .mat file become keys in the loaded dictionary\n",
    "x = mat_data['x']\n",
    "t = mat_data['t']\n",
    "u = mat_data['u']\n",
    "\n",
    "# Use the loaded variables as needed\n",
    "print(x.shape)\n",
    "print(t.shape)\n",
    "print(u.shape)\n",
    "\n",
    "X, T = np.meshgrid(x, t)\n",
    "# Define custom color levels\n",
    "c_levels = np.linspace(np.min(u), np.max(u), 100)\n",
    "\n",
    "# Plot the contour\n",
    "plt.figure()\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.contourf(T, X, u, levels=c_levels, cmap='coolwarm')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.title('Allen-cahn-Equation')\n",
    "plt.colorbar()  # Add a colorbar for the contour levels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88b76c79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the .mat file\n",
    "mat_data = scipy.io.loadmat('X.mat')\n",
    "\n",
    "X = mat_data['X']\n",
    "\n",
    "mat_data1 = scipy.io.loadmat('y_pred.mat')\n",
    "\n",
    "u1 = mat_data1['y_pred']\n",
    "\n",
    "np.set_printoptions(threshold=np.inf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f35d35a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "u1 = u1.reshape(101, 201)\n",
    "u1_new = u1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bbdcc6b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X, T = np.meshgrid(x, t)\n",
    "\n",
    "# # Define custom color levels\n",
    "c_levels = np.linspace(np.min(u1_new),  np.max(u1_new), 100)\n",
    "\n",
    "# Plot the contour\n",
    "plt.figure()\n",
    "plt.figure(figsize=(15, 5))\n",
    "plt.contourf(T, X, u1_new.T, levels=c_levels, cmap='coolwarm')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('x')\n",
    "plt.title('Allen-cahn-Equation')\n",
    "plt.colorbar()  # Add a colorbar for the contour levels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a0d65ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Toy problem data\n",
    "input_size = 201  # number of columns in a dataset\n",
    "hidden_size = 32  # number of neurons\n",
    "output_size = 201\n",
    "sequence_length = 80  # number of sequences/ number of rows\n",
    "batch_size = 1\n",
    "num_epochs = 20000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ae65e203",
   "metadata": {},
   "outputs": [],
   "source": [
    "u1 = u1_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "36ea25b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test data shape (201,)\n",
      "input data shape (201, 80)\n",
      "Target data shape (201, 80)\n"
     ]
    }
   ],
   "source": [
    "input_data = u1[:, 0:80]\n",
    "target_data = u1[:, 1:81]\n",
    "\n",
    "test_data = u1[:, 80] ### Change here\n",
    "#test_target = u1[:, 81:101]\n",
    "\n",
    "print(\"test data shape\", test_data.shape)\n",
    "#print(\"test target shape\", test_target.shape)\n",
    "\n",
    "print(\"input data shape\",input_data.shape)\n",
    "print(\"Target data shape\",target_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce742851",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input tensor shape torch.Size([1, 80, 201])\n",
      "Target tensor shape torch.Size([1, 80, 201])\n"
     ]
    }
   ],
   "source": [
    "# Convert data to tensors\n",
    "input_tensor = torch.tensor(input_data.T).view(batch_size, sequence_length, input_size).float()\n",
    "target_tensor = torch.tensor(target_data.T).view(batch_size, sequence_length, output_size).float()\n",
    "\n",
    "print(\"input tensor shape\",input_tensor.shape)\n",
    "print(\"Target tensor shape\",target_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b7d82d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert test data to tensors\n",
    "test_tensor = torch.tensor(test_data.T).view(batch_size, 1, input_size).float()\n",
    "#test_target_tensor = torch.tensor(test_target.T).view(batch_size, 20, output_size).float()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e80583ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10/20000, Loss: 0.0576\n",
      "Epoch: 20/20000, Loss: 0.0298\n",
      "Epoch: 30/20000, Loss: 0.0124\n",
      "Epoch: 40/20000, Loss: 0.0043\n",
      "Epoch: 50/20000, Loss: 0.0025\n",
      "Epoch: 60/20000, Loss: 0.0019\n",
      "Epoch: 70/20000, Loss: 0.0015\n",
      "Epoch: 80/20000, Loss: 0.0012\n",
      "Epoch: 90/20000, Loss: 0.0010\n",
      "Epoch: 100/20000, Loss: 0.0007\n",
      "Epoch: 110/20000, Loss: 0.0006\n",
      "Epoch: 120/20000, Loss: 0.0004\n",
      "Epoch: 130/20000, Loss: 0.0004\n",
      "Epoch: 140/20000, Loss: 0.0003\n",
      "Epoch: 150/20000, Loss: 0.0003\n",
      "Epoch: 160/20000, Loss: 0.0003\n",
      "Epoch: 170/20000, Loss: 0.0002\n",
      "Epoch: 180/20000, Loss: 0.0002\n",
      "Epoch: 190/20000, Loss: 0.0002\n",
      "Epoch: 200/20000, Loss: 0.0002\n",
      "Epoch: 210/20000, Loss: 0.0002\n",
      "Epoch: 220/20000, Loss: 0.0002\n",
      "Epoch: 230/20000, Loss: 0.0002\n",
      "Epoch: 240/20000, Loss: 0.0003\n",
      "Epoch: 250/20000, Loss: 0.0002\n",
      "Epoch: 260/20000, Loss: 0.0001\n",
      "Epoch: 270/20000, Loss: 0.0001\n",
      "Epoch: 280/20000, Loss: 0.0001\n",
      "Epoch: 290/20000, Loss: 0.0001\n",
      "Epoch: 300/20000, Loss: 0.0001\n",
      "Epoch: 310/20000, Loss: 0.0001\n",
      "Epoch: 320/20000, Loss: 0.0001\n",
      "Epoch: 330/20000, Loss: 0.0002\n",
      "Epoch: 340/20000, Loss: 0.0001\n",
      "Epoch: 350/20000, Loss: 0.0001\n",
      "Epoch: 360/20000, Loss: 0.0001\n",
      "Epoch: 370/20000, Loss: 0.0001\n",
      "Epoch: 380/20000, Loss: 0.0001\n",
      "Epoch: 390/20000, Loss: 0.0001\n",
      "Epoch: 400/20000, Loss: 0.0001\n",
      "Epoch: 410/20000, Loss: 0.0001\n",
      "Epoch: 420/20000, Loss: 0.0003\n",
      "Epoch: 430/20000, Loss: 0.0001\n",
      "Epoch: 440/20000, Loss: 0.0001\n",
      "Epoch: 450/20000, Loss: 0.0000\n",
      "Epoch: 460/20000, Loss: 0.0000\n",
      "Epoch: 470/20000, Loss: 0.0000\n",
      "Epoch: 480/20000, Loss: 0.0000\n",
      "Epoch: 490/20000, Loss: 0.0000\n",
      "Epoch: 500/20000, Loss: 0.0000\n",
      "Epoch: 510/20000, Loss: 0.0000\n",
      "Epoch: 520/20000, Loss: 0.0000\n",
      "Epoch: 530/20000, Loss: 0.0000\n",
      "Epoch: 540/20000, Loss: 0.0000\n",
      "Epoch: 550/20000, Loss: 0.0000\n",
      "Epoch: 560/20000, Loss: 0.0003\n",
      "Epoch: 570/20000, Loss: 0.0001\n",
      "Epoch: 580/20000, Loss: 0.0000\n",
      "Epoch: 590/20000, Loss: 0.0000\n",
      "Epoch: 600/20000, Loss: 0.0000\n",
      "Epoch: 610/20000, Loss: 0.0000\n",
      "Epoch: 620/20000, Loss: 0.0000\n",
      "Epoch: 630/20000, Loss: 0.0000\n",
      "Epoch: 640/20000, Loss: 0.0000\n",
      "Epoch: 650/20000, Loss: 0.0000\n",
      "Epoch: 660/20000, Loss: 0.0000\n",
      "Epoch: 670/20000, Loss: 0.0000\n",
      "Epoch: 680/20000, Loss: 0.0000\n",
      "Epoch: 690/20000, Loss: 0.0000\n",
      "Epoch: 700/20000, Loss: 0.0000\n",
      "Epoch: 710/20000, Loss: 0.0000\n",
      "Epoch: 720/20000, Loss: 0.0000\n",
      "Epoch: 730/20000, Loss: 0.0000\n",
      "Epoch: 740/20000, Loss: 0.0004\n",
      "Epoch: 750/20000, Loss: 0.0001\n",
      "Epoch: 760/20000, Loss: 0.0001\n",
      "Epoch: 770/20000, Loss: 0.0000\n",
      "Epoch: 780/20000, Loss: 0.0000\n",
      "Epoch: 790/20000, Loss: 0.0000\n",
      "Epoch: 800/20000, Loss: 0.0000\n",
      "Epoch: 810/20000, Loss: 0.0000\n",
      "Epoch: 820/20000, Loss: 0.0000\n",
      "Epoch: 830/20000, Loss: 0.0000\n",
      "Epoch: 840/20000, Loss: 0.0000\n",
      "Epoch: 850/20000, Loss: 0.0000\n",
      "Epoch: 860/20000, Loss: 0.0000\n",
      "Epoch: 870/20000, Loss: 0.0000\n",
      "Epoch: 880/20000, Loss: 0.0000\n",
      "Epoch: 890/20000, Loss: 0.0000\n",
      "Epoch: 900/20000, Loss: 0.0000\n",
      "Epoch: 910/20000, Loss: 0.0000\n",
      "Epoch: 920/20000, Loss: 0.0000\n",
      "Epoch: 930/20000, Loss: 0.0000\n",
      "Epoch: 940/20000, Loss: 0.0000\n",
      "Epoch: 950/20000, Loss: 0.0000\n",
      "Epoch: 960/20000, Loss: 0.0000\n",
      "Epoch: 970/20000, Loss: 0.0000\n",
      "Epoch: 980/20000, Loss: 0.0000\n",
      "Epoch: 990/20000, Loss: 0.0000\n",
      "Epoch: 1000/20000, Loss: 0.0004\n",
      "Epoch: 1010/20000, Loss: 0.0001\n",
      "Epoch: 1020/20000, Loss: 0.0000\n",
      "Epoch: 1030/20000, Loss: 0.0000\n",
      "Epoch: 1040/20000, Loss: 0.0000\n",
      "Epoch: 1050/20000, Loss: 0.0000\n",
      "Epoch: 1060/20000, Loss: 0.0000\n",
      "Epoch: 1070/20000, Loss: 0.0000\n",
      "Epoch: 1080/20000, Loss: 0.0000\n",
      "Epoch: 1090/20000, Loss: 0.0000\n",
      "Epoch: 1100/20000, Loss: 0.0000\n",
      "Epoch: 1110/20000, Loss: 0.0000\n",
      "Epoch: 1120/20000, Loss: 0.0000\n",
      "Epoch: 1130/20000, Loss: 0.0000\n",
      "Epoch: 1140/20000, Loss: 0.0000\n",
      "Epoch: 1150/20000, Loss: 0.0000\n",
      "Epoch: 1160/20000, Loss: 0.0000\n",
      "Epoch: 1170/20000, Loss: 0.0000\n",
      "Epoch: 1180/20000, Loss: 0.0000\n",
      "Epoch: 1190/20000, Loss: 0.0000\n",
      "Epoch: 1200/20000, Loss: 0.0000\n",
      "Epoch: 1210/20000, Loss: 0.0000\n",
      "Epoch: 1220/20000, Loss: 0.0000\n",
      "Epoch: 1230/20000, Loss: 0.0000\n",
      "Epoch: 1240/20000, Loss: 0.0000\n",
      "Epoch: 1250/20000, Loss: 0.0000\n",
      "Epoch: 1260/20000, Loss: 0.0000\n",
      "Epoch: 1270/20000, Loss: 0.0000\n",
      "Epoch: 1280/20000, Loss: 0.0000\n",
      "Epoch: 1290/20000, Loss: 0.0000\n",
      "Epoch: 1300/20000, Loss: 0.0000\n",
      "Epoch: 1310/20000, Loss: 0.0000\n",
      "Epoch: 1320/20000, Loss: 0.0000\n",
      "Epoch: 1330/20000, Loss: 0.0000\n",
      "Epoch: 1340/20000, Loss: 0.0000\n",
      "Epoch: 1350/20000, Loss: 0.0000\n",
      "Epoch: 1360/20000, Loss: 0.0000\n",
      "Epoch: 1370/20000, Loss: 0.0002\n",
      "Epoch: 1380/20000, Loss: 0.0001\n",
      "Epoch: 1390/20000, Loss: 0.0000\n",
      "Epoch: 1400/20000, Loss: 0.0000\n",
      "Epoch: 1410/20000, Loss: 0.0000\n",
      "Epoch: 1420/20000, Loss: 0.0000\n",
      "Epoch: 1430/20000, Loss: 0.0000\n",
      "Epoch: 1440/20000, Loss: 0.0000\n",
      "Epoch: 1450/20000, Loss: 0.0000\n",
      "Epoch: 1460/20000, Loss: 0.0000\n",
      "Epoch: 1470/20000, Loss: 0.0000\n",
      "Epoch: 1480/20000, Loss: 0.0000\n",
      "Epoch: 1490/20000, Loss: 0.0000\n",
      "Epoch: 1500/20000, Loss: 0.0000\n",
      "Epoch: 1510/20000, Loss: 0.0000\n",
      "Epoch: 1520/20000, Loss: 0.0000\n",
      "Epoch: 1530/20000, Loss: 0.0000\n",
      "Epoch: 1540/20000, Loss: 0.0000\n",
      "Epoch: 1550/20000, Loss: 0.0000\n",
      "Epoch: 1560/20000, Loss: 0.0000\n",
      "Epoch: 1570/20000, Loss: 0.0000\n",
      "Epoch: 1580/20000, Loss: 0.0000\n",
      "Epoch: 1590/20000, Loss: 0.0000\n",
      "Epoch: 1600/20000, Loss: 0.0000\n",
      "Epoch: 1610/20000, Loss: 0.0000\n",
      "Epoch: 1620/20000, Loss: 0.0001\n",
      "Epoch: 1630/20000, Loss: 0.0000\n",
      "Epoch: 1640/20000, Loss: 0.0000\n",
      "Epoch: 1650/20000, Loss: 0.0000\n",
      "Epoch: 1660/20000, Loss: 0.0000\n",
      "Epoch: 1670/20000, Loss: 0.0000\n",
      "Epoch: 1680/20000, Loss: 0.0000\n",
      "Epoch: 1690/20000, Loss: 0.0000\n",
      "Epoch: 1700/20000, Loss: 0.0000\n",
      "Epoch: 1710/20000, Loss: 0.0000\n",
      "Epoch: 1720/20000, Loss: 0.0000\n",
      "Epoch: 1730/20000, Loss: 0.0000\n",
      "Epoch: 1740/20000, Loss: 0.0000\n",
      "Epoch: 1750/20000, Loss: 0.0000\n",
      "Epoch: 1760/20000, Loss: 0.0000\n",
      "Epoch: 1770/20000, Loss: 0.0000\n",
      "Epoch: 1780/20000, Loss: 0.0000\n",
      "Epoch: 1790/20000, Loss: 0.0000\n",
      "Epoch: 1800/20000, Loss: 0.0000\n",
      "Epoch: 1810/20000, Loss: 0.0000\n",
      "Epoch: 1820/20000, Loss: 0.0000\n",
      "Epoch: 1830/20000, Loss: 0.0000\n",
      "Epoch: 1840/20000, Loss: 0.0000\n",
      "Epoch: 1850/20000, Loss: 0.0000\n",
      "Epoch: 1860/20000, Loss: 0.0000\n",
      "Epoch: 1870/20000, Loss: 0.0001\n",
      "Epoch: 1880/20000, Loss: 0.0001\n",
      "Epoch: 1890/20000, Loss: 0.0001\n",
      "Epoch: 1900/20000, Loss: 0.0000\n",
      "Epoch: 1910/20000, Loss: 0.0000\n",
      "Epoch: 1920/20000, Loss: 0.0000\n",
      "Epoch: 1930/20000, Loss: 0.0000\n",
      "Epoch: 1940/20000, Loss: 0.0000\n",
      "Epoch: 1950/20000, Loss: 0.0000\n",
      "Epoch: 1960/20000, Loss: 0.0000\n",
      "Epoch: 1970/20000, Loss: 0.0000\n",
      "Epoch: 1980/20000, Loss: 0.0000\n",
      "Epoch: 1990/20000, Loss: 0.0000\n",
      "Epoch: 2000/20000, Loss: 0.0000\n",
      "Epoch: 2010/20000, Loss: 0.0000\n",
      "Epoch: 2020/20000, Loss: 0.0000\n",
      "Epoch: 2030/20000, Loss: 0.0000\n",
      "Epoch: 2040/20000, Loss: 0.0000\n",
      "Epoch: 2050/20000, Loss: 0.0000\n",
      "Epoch: 2060/20000, Loss: 0.0000\n",
      "Epoch: 2070/20000, Loss: 0.0000\n",
      "Epoch: 2080/20000, Loss: 0.0002\n",
      "Epoch: 2090/20000, Loss: 0.0000\n",
      "Epoch: 2100/20000, Loss: 0.0000\n",
      "Epoch: 2110/20000, Loss: 0.0000\n",
      "Epoch: 2120/20000, Loss: 0.0000\n",
      "Epoch: 2130/20000, Loss: 0.0000\n",
      "Epoch: 2140/20000, Loss: 0.0000\n",
      "Epoch: 2150/20000, Loss: 0.0000\n",
      "Epoch: 2160/20000, Loss: 0.0000\n",
      "Epoch: 2170/20000, Loss: 0.0000\n",
      "Epoch: 2180/20000, Loss: 0.0000\n",
      "Epoch: 2190/20000, Loss: 0.0000\n",
      "Epoch: 2200/20000, Loss: 0.0000\n",
      "Epoch: 2210/20000, Loss: 0.0000\n",
      "Epoch: 2220/20000, Loss: 0.0000\n",
      "Epoch: 2230/20000, Loss: 0.0000\n",
      "Epoch: 2240/20000, Loss: 0.0000\n",
      "Epoch: 2250/20000, Loss: 0.0000\n",
      "Epoch: 2260/20000, Loss: 0.0000\n",
      "Epoch: 2270/20000, Loss: 0.0000\n",
      "Epoch: 2280/20000, Loss: 0.0000\n",
      "Epoch: 2290/20000, Loss: 0.0003\n",
      "Epoch: 2300/20000, Loss: 0.0000\n",
      "Epoch: 2310/20000, Loss: 0.0000\n",
      "Epoch: 2320/20000, Loss: 0.0000\n",
      "Epoch: 2330/20000, Loss: 0.0000\n",
      "Epoch: 2340/20000, Loss: 0.0000\n",
      "Epoch: 2350/20000, Loss: 0.0000\n",
      "Epoch: 2360/20000, Loss: 0.0000\n",
      "Epoch: 2370/20000, Loss: 0.0000\n",
      "Epoch: 2380/20000, Loss: 0.0000\n",
      "Epoch: 2390/20000, Loss: 0.0000\n",
      "Epoch: 2400/20000, Loss: 0.0000\n",
      "Epoch: 2410/20000, Loss: 0.0000\n",
      "Epoch: 2420/20000, Loss: 0.0000\n",
      "Epoch: 2430/20000, Loss: 0.0000\n",
      "Epoch: 2440/20000, Loss: 0.0000\n",
      "Epoch: 2450/20000, Loss: 0.0000\n",
      "Epoch: 2460/20000, Loss: 0.0000\n",
      "Epoch: 2470/20000, Loss: 0.0000\n",
      "Epoch: 2480/20000, Loss: 0.0000\n",
      "Epoch: 2490/20000, Loss: 0.0002\n",
      "Epoch: 2500/20000, Loss: 0.0000\n",
      "Epoch: 2510/20000, Loss: 0.0000\n",
      "Epoch: 2520/20000, Loss: 0.0000\n",
      "Epoch: 2530/20000, Loss: 0.0000\n",
      "Epoch: 2540/20000, Loss: 0.0000\n",
      "Epoch: 2550/20000, Loss: 0.0000\n",
      "Epoch: 2560/20000, Loss: 0.0000\n",
      "Epoch: 2570/20000, Loss: 0.0000\n",
      "Epoch: 2580/20000, Loss: 0.0000\n",
      "Epoch: 2590/20000, Loss: 0.0000\n",
      "Epoch: 2600/20000, Loss: 0.0000\n",
      "Epoch: 2610/20000, Loss: 0.0000\n",
      "Epoch: 2620/20000, Loss: 0.0000\n",
      "Epoch: 2630/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2640/20000, Loss: 0.0000\n",
      "Epoch: 2650/20000, Loss: 0.0000\n",
      "Epoch: 2660/20000, Loss: 0.0000\n",
      "Epoch: 2670/20000, Loss: 0.0000\n",
      "Epoch: 2680/20000, Loss: 0.0000\n",
      "Epoch: 2690/20000, Loss: 0.0000\n",
      "Epoch: 2700/20000, Loss: 0.0000\n",
      "Epoch: 2710/20000, Loss: 0.0000\n",
      "Epoch: 2720/20000, Loss: 0.0000\n",
      "Epoch: 2730/20000, Loss: 0.0000\n",
      "Epoch: 2740/20000, Loss: 0.0000\n",
      "Epoch: 2750/20000, Loss: 0.0000\n",
      "Epoch: 2760/20000, Loss: 0.0000\n",
      "Epoch: 2770/20000, Loss: 0.0000\n",
      "Epoch: 2780/20000, Loss: 0.0000\n",
      "Epoch: 2790/20000, Loss: 0.0000\n",
      "Epoch: 2800/20000, Loss: 0.0000\n",
      "Epoch: 2810/20000, Loss: 0.0000\n",
      "Epoch: 2820/20000, Loss: 0.0000\n",
      "Epoch: 2830/20000, Loss: 0.0000\n",
      "Epoch: 2840/20000, Loss: 0.0001\n",
      "Epoch: 2850/20000, Loss: 0.0000\n",
      "Epoch: 2860/20000, Loss: 0.0000\n",
      "Epoch: 2870/20000, Loss: 0.0000\n",
      "Epoch: 2880/20000, Loss: 0.0000\n",
      "Epoch: 2890/20000, Loss: 0.0000\n",
      "Epoch: 2900/20000, Loss: 0.0000\n",
      "Epoch: 2910/20000, Loss: 0.0000\n",
      "Epoch: 2920/20000, Loss: 0.0000\n",
      "Epoch: 2930/20000, Loss: 0.0000\n",
      "Epoch: 2940/20000, Loss: 0.0000\n",
      "Epoch: 2950/20000, Loss: 0.0000\n",
      "Epoch: 2960/20000, Loss: 0.0000\n",
      "Epoch: 2970/20000, Loss: 0.0000\n",
      "Epoch: 2980/20000, Loss: 0.0000\n",
      "Epoch: 2990/20000, Loss: 0.0000\n",
      "Epoch: 3000/20000, Loss: 0.0000\n",
      "Epoch: 3010/20000, Loss: 0.0000\n",
      "Epoch: 3020/20000, Loss: 0.0002\n",
      "Epoch: 3030/20000, Loss: 0.0001\n",
      "Epoch: 3040/20000, Loss: 0.0000\n",
      "Epoch: 3050/20000, Loss: 0.0000\n",
      "Epoch: 3060/20000, Loss: 0.0000\n",
      "Epoch: 3070/20000, Loss: 0.0000\n",
      "Epoch: 3080/20000, Loss: 0.0000\n",
      "Epoch: 3090/20000, Loss: 0.0000\n",
      "Epoch: 3100/20000, Loss: 0.0000\n",
      "Epoch: 3110/20000, Loss: 0.0000\n",
      "Epoch: 3120/20000, Loss: 0.0000\n",
      "Epoch: 3130/20000, Loss: 0.0000\n",
      "Epoch: 3140/20000, Loss: 0.0000\n",
      "Epoch: 3150/20000, Loss: 0.0000\n",
      "Epoch: 3160/20000, Loss: 0.0000\n",
      "Epoch: 3170/20000, Loss: 0.0000\n",
      "Epoch: 3180/20000, Loss: 0.0000\n",
      "Epoch: 3190/20000, Loss: 0.0000\n",
      "Epoch: 3200/20000, Loss: 0.0000\n",
      "Epoch: 3210/20000, Loss: 0.0003\n",
      "Epoch: 3220/20000, Loss: 0.0000\n",
      "Epoch: 3230/20000, Loss: 0.0000\n",
      "Epoch: 3240/20000, Loss: 0.0000\n",
      "Epoch: 3250/20000, Loss: 0.0000\n",
      "Epoch: 3260/20000, Loss: 0.0000\n",
      "Epoch: 3270/20000, Loss: 0.0000\n",
      "Epoch: 3280/20000, Loss: 0.0000\n",
      "Epoch: 3290/20000, Loss: 0.0000\n",
      "Epoch: 3300/20000, Loss: 0.0000\n",
      "Epoch: 3310/20000, Loss: 0.0000\n",
      "Epoch: 3320/20000, Loss: 0.0000\n",
      "Epoch: 3330/20000, Loss: 0.0000\n",
      "Epoch: 3340/20000, Loss: 0.0000\n",
      "Epoch: 3350/20000, Loss: 0.0000\n",
      "Epoch: 3360/20000, Loss: 0.0000\n",
      "Epoch: 3370/20000, Loss: 0.0000\n",
      "Epoch: 3380/20000, Loss: 0.0000\n",
      "Epoch: 3390/20000, Loss: 0.0000\n",
      "Epoch: 3400/20000, Loss: 0.0001\n",
      "Epoch: 3410/20000, Loss: 0.0001\n",
      "Epoch: 3420/20000, Loss: 0.0000\n",
      "Epoch: 3430/20000, Loss: 0.0000\n",
      "Epoch: 3440/20000, Loss: 0.0000\n",
      "Epoch: 3450/20000, Loss: 0.0000\n",
      "Epoch: 3460/20000, Loss: 0.0000\n",
      "Epoch: 3470/20000, Loss: 0.0000\n",
      "Epoch: 3480/20000, Loss: 0.0000\n",
      "Epoch: 3490/20000, Loss: 0.0000\n",
      "Epoch: 3500/20000, Loss: 0.0000\n",
      "Epoch: 3510/20000, Loss: 0.0000\n",
      "Epoch: 3520/20000, Loss: 0.0000\n",
      "Epoch: 3530/20000, Loss: 0.0000\n",
      "Epoch: 3540/20000, Loss: 0.0000\n",
      "Epoch: 3550/20000, Loss: 0.0000\n",
      "Epoch: 3560/20000, Loss: 0.0000\n",
      "Epoch: 3570/20000, Loss: 0.0000\n",
      "Epoch: 3580/20000, Loss: 0.0000\n",
      "Epoch: 3590/20000, Loss: 0.0000\n",
      "Epoch: 3600/20000, Loss: 0.0000\n",
      "Epoch: 3610/20000, Loss: 0.0000\n",
      "Epoch: 3620/20000, Loss: 0.0000\n",
      "Epoch: 3630/20000, Loss: 0.0001\n",
      "Epoch: 3640/20000, Loss: 0.0000\n",
      "Epoch: 3650/20000, Loss: 0.0000\n",
      "Epoch: 3660/20000, Loss: 0.0000\n",
      "Epoch: 3670/20000, Loss: 0.0000\n",
      "Epoch: 3680/20000, Loss: 0.0000\n",
      "Epoch: 3690/20000, Loss: 0.0000\n",
      "Epoch: 3700/20000, Loss: 0.0000\n",
      "Epoch: 3710/20000, Loss: 0.0000\n",
      "Epoch: 3720/20000, Loss: 0.0000\n",
      "Epoch: 3730/20000, Loss: 0.0000\n",
      "Epoch: 3740/20000, Loss: 0.0000\n",
      "Epoch: 3750/20000, Loss: 0.0000\n",
      "Epoch: 3760/20000, Loss: 0.0000\n",
      "Epoch: 3770/20000, Loss: 0.0000\n",
      "Epoch: 3780/20000, Loss: 0.0000\n",
      "Epoch: 3790/20000, Loss: 0.0000\n",
      "Epoch: 3800/20000, Loss: 0.0000\n",
      "Epoch: 3810/20000, Loss: 0.0000\n",
      "Epoch: 3820/20000, Loss: 0.0000\n",
      "Epoch: 3830/20000, Loss: 0.0000\n",
      "Epoch: 3840/20000, Loss: 0.0000\n",
      "Epoch: 3850/20000, Loss: 0.0000\n",
      "Epoch: 3860/20000, Loss: 0.0000\n",
      "Epoch: 3870/20000, Loss: 0.0000\n",
      "Epoch: 3880/20000, Loss: 0.0000\n",
      "Epoch: 3890/20000, Loss: 0.0000\n",
      "Epoch: 3900/20000, Loss: 0.0000\n",
      "Epoch: 3910/20000, Loss: 0.0000\n",
      "Epoch: 3920/20000, Loss: 0.0000\n",
      "Epoch: 3930/20000, Loss: 0.0000\n",
      "Epoch: 3940/20000, Loss: 0.0000\n",
      "Epoch: 3950/20000, Loss: 0.0000\n",
      "Epoch: 3960/20000, Loss: 0.0000\n",
      "Epoch: 3970/20000, Loss: 0.0000\n",
      "Epoch: 3980/20000, Loss: 0.0000\n",
      "Epoch: 3990/20000, Loss: 0.0000\n",
      "Epoch: 4000/20000, Loss: 0.0000\n",
      "Epoch: 4010/20000, Loss: 0.0000\n",
      "Epoch: 4020/20000, Loss: 0.0000\n",
      "Epoch: 4030/20000, Loss: 0.0000\n",
      "Epoch: 4040/20000, Loss: 0.0000\n",
      "Epoch: 4050/20000, Loss: 0.0000\n",
      "Epoch: 4060/20000, Loss: 0.0003\n",
      "Epoch: 4070/20000, Loss: 0.0001\n",
      "Epoch: 4080/20000, Loss: 0.0000\n",
      "Epoch: 4090/20000, Loss: 0.0000\n",
      "Epoch: 4100/20000, Loss: 0.0000\n",
      "Epoch: 4110/20000, Loss: 0.0000\n",
      "Epoch: 4120/20000, Loss: 0.0000\n",
      "Epoch: 4130/20000, Loss: 0.0000\n",
      "Epoch: 4140/20000, Loss: 0.0000\n",
      "Epoch: 4150/20000, Loss: 0.0000\n",
      "Epoch: 4160/20000, Loss: 0.0000\n",
      "Epoch: 4170/20000, Loss: 0.0000\n",
      "Epoch: 4180/20000, Loss: 0.0000\n",
      "Epoch: 4190/20000, Loss: 0.0000\n",
      "Epoch: 4200/20000, Loss: 0.0000\n",
      "Epoch: 4210/20000, Loss: 0.0000\n",
      "Epoch: 4220/20000, Loss: 0.0000\n",
      "Epoch: 4230/20000, Loss: 0.0002\n",
      "Epoch: 4240/20000, Loss: 0.0000\n",
      "Epoch: 4250/20000, Loss: 0.0000\n",
      "Epoch: 4260/20000, Loss: 0.0000\n",
      "Epoch: 4270/20000, Loss: 0.0000\n",
      "Epoch: 4280/20000, Loss: 0.0000\n",
      "Epoch: 4290/20000, Loss: 0.0000\n",
      "Epoch: 4300/20000, Loss: 0.0000\n",
      "Epoch: 4310/20000, Loss: 0.0000\n",
      "Epoch: 4320/20000, Loss: 0.0000\n",
      "Epoch: 4330/20000, Loss: 0.0000\n",
      "Epoch: 4340/20000, Loss: 0.0000\n",
      "Epoch: 4350/20000, Loss: 0.0000\n",
      "Epoch: 4360/20000, Loss: 0.0000\n",
      "Epoch: 4370/20000, Loss: 0.0000\n",
      "Epoch: 4380/20000, Loss: 0.0002\n",
      "Epoch: 4390/20000, Loss: 0.0000\n",
      "Epoch: 4400/20000, Loss: 0.0000\n",
      "Epoch: 4410/20000, Loss: 0.0000\n",
      "Epoch: 4420/20000, Loss: 0.0000\n",
      "Epoch: 4430/20000, Loss: 0.0000\n",
      "Epoch: 4440/20000, Loss: 0.0000\n",
      "Epoch: 4450/20000, Loss: 0.0000\n",
      "Epoch: 4460/20000, Loss: 0.0000\n",
      "Epoch: 4470/20000, Loss: 0.0000\n",
      "Epoch: 4480/20000, Loss: 0.0000\n",
      "Epoch: 4490/20000, Loss: 0.0000\n",
      "Epoch: 4500/20000, Loss: 0.0000\n",
      "Epoch: 4510/20000, Loss: 0.0000\n",
      "Epoch: 4520/20000, Loss: 0.0000\n",
      "Epoch: 4530/20000, Loss: 0.0000\n",
      "Epoch: 4540/20000, Loss: 0.0002\n",
      "Epoch: 4550/20000, Loss: 0.0000\n",
      "Epoch: 4560/20000, Loss: 0.0000\n",
      "Epoch: 4570/20000, Loss: 0.0000\n",
      "Epoch: 4580/20000, Loss: 0.0000\n",
      "Epoch: 4590/20000, Loss: 0.0000\n",
      "Epoch: 4600/20000, Loss: 0.0000\n",
      "Epoch: 4610/20000, Loss: 0.0000\n",
      "Epoch: 4620/20000, Loss: 0.0000\n",
      "Epoch: 4630/20000, Loss: 0.0000\n",
      "Epoch: 4640/20000, Loss: 0.0000\n",
      "Epoch: 4650/20000, Loss: 0.0001\n",
      "Epoch: 4660/20000, Loss: 0.0000\n",
      "Epoch: 4670/20000, Loss: 0.0000\n",
      "Epoch: 4680/20000, Loss: 0.0000\n",
      "Epoch: 4690/20000, Loss: 0.0000\n",
      "Epoch: 4700/20000, Loss: 0.0000\n",
      "Epoch: 4710/20000, Loss: 0.0000\n",
      "Epoch: 4720/20000, Loss: 0.0000\n",
      "Epoch: 4730/20000, Loss: 0.0001\n",
      "Epoch: 4740/20000, Loss: 0.0000\n",
      "Epoch: 4750/20000, Loss: 0.0000\n",
      "Epoch: 4760/20000, Loss: 0.0000\n",
      "Epoch: 4770/20000, Loss: 0.0000\n",
      "Epoch: 4780/20000, Loss: 0.0000\n",
      "Epoch: 4790/20000, Loss: 0.0000\n",
      "Epoch: 4800/20000, Loss: 0.0000\n",
      "Epoch: 4810/20000, Loss: 0.0000\n",
      "Epoch: 4820/20000, Loss: 0.0000\n",
      "Epoch: 4830/20000, Loss: 0.0000\n",
      "Epoch: 4840/20000, Loss: 0.0000\n",
      "Epoch: 4850/20000, Loss: 0.0000\n",
      "Epoch: 4860/20000, Loss: 0.0001\n",
      "Epoch: 4870/20000, Loss: 0.0000\n",
      "Epoch: 4880/20000, Loss: 0.0000\n",
      "Epoch: 4890/20000, Loss: 0.0000\n",
      "Epoch: 4900/20000, Loss: 0.0000\n",
      "Epoch: 4910/20000, Loss: 0.0000\n",
      "Epoch: 4920/20000, Loss: 0.0000\n",
      "Epoch: 4930/20000, Loss: 0.0000\n",
      "Epoch: 4940/20000, Loss: 0.0000\n",
      "Epoch: 4950/20000, Loss: 0.0000\n",
      "Epoch: 4960/20000, Loss: 0.0000\n",
      "Epoch: 4970/20000, Loss: 0.0000\n",
      "Epoch: 4980/20000, Loss: 0.0000\n",
      "Epoch: 4990/20000, Loss: 0.0000\n",
      "Epoch: 5000/20000, Loss: 0.0000\n",
      "Epoch: 5010/20000, Loss: 0.0000\n",
      "Epoch: 5020/20000, Loss: 0.0000\n",
      "Epoch: 5030/20000, Loss: 0.0000\n",
      "Epoch: 5040/20000, Loss: 0.0000\n",
      "Epoch: 5050/20000, Loss: 0.0000\n",
      "Epoch: 5060/20000, Loss: 0.0001\n",
      "Epoch: 5070/20000, Loss: 0.0001\n",
      "Epoch: 5080/20000, Loss: 0.0000\n",
      "Epoch: 5090/20000, Loss: 0.0000\n",
      "Epoch: 5100/20000, Loss: 0.0000\n",
      "Epoch: 5110/20000, Loss: 0.0000\n",
      "Epoch: 5120/20000, Loss: 0.0000\n",
      "Epoch: 5130/20000, Loss: 0.0000\n",
      "Epoch: 5140/20000, Loss: 0.0000\n",
      "Epoch: 5150/20000, Loss: 0.0000\n",
      "Epoch: 5160/20000, Loss: 0.0000\n",
      "Epoch: 5170/20000, Loss: 0.0000\n",
      "Epoch: 5180/20000, Loss: 0.0000\n",
      "Epoch: 5190/20000, Loss: 0.0000\n",
      "Epoch: 5200/20000, Loss: 0.0000\n",
      "Epoch: 5210/20000, Loss: 0.0000\n",
      "Epoch: 5220/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5230/20000, Loss: 0.0000\n",
      "Epoch: 5240/20000, Loss: 0.0001\n",
      "Epoch: 5250/20000, Loss: 0.0001\n",
      "Epoch: 5260/20000, Loss: 0.0000\n",
      "Epoch: 5270/20000, Loss: 0.0000\n",
      "Epoch: 5280/20000, Loss: 0.0000\n",
      "Epoch: 5290/20000, Loss: 0.0000\n",
      "Epoch: 5300/20000, Loss: 0.0000\n",
      "Epoch: 5310/20000, Loss: 0.0000\n",
      "Epoch: 5320/20000, Loss: 0.0000\n",
      "Epoch: 5330/20000, Loss: 0.0000\n",
      "Epoch: 5340/20000, Loss: 0.0000\n",
      "Epoch: 5350/20000, Loss: 0.0000\n",
      "Epoch: 5360/20000, Loss: 0.0000\n",
      "Epoch: 5370/20000, Loss: 0.0001\n",
      "Epoch: 5380/20000, Loss: 0.0000\n",
      "Epoch: 5390/20000, Loss: 0.0000\n",
      "Epoch: 5400/20000, Loss: 0.0000\n",
      "Epoch: 5410/20000, Loss: 0.0000\n",
      "Epoch: 5420/20000, Loss: 0.0000\n",
      "Epoch: 5430/20000, Loss: 0.0000\n",
      "Epoch: 5440/20000, Loss: 0.0000\n",
      "Epoch: 5450/20000, Loss: 0.0000\n",
      "Epoch: 5460/20000, Loss: 0.0000\n",
      "Epoch: 5470/20000, Loss: 0.0000\n",
      "Epoch: 5480/20000, Loss: 0.0000\n",
      "Epoch: 5490/20000, Loss: 0.0000\n",
      "Epoch: 5500/20000, Loss: 0.0000\n",
      "Epoch: 5510/20000, Loss: 0.0001\n",
      "Epoch: 5520/20000, Loss: 0.0000\n",
      "Epoch: 5530/20000, Loss: 0.0000\n",
      "Epoch: 5540/20000, Loss: 0.0000\n",
      "Epoch: 5550/20000, Loss: 0.0000\n",
      "Epoch: 5560/20000, Loss: 0.0000\n",
      "Epoch: 5570/20000, Loss: 0.0000\n",
      "Epoch: 5580/20000, Loss: 0.0000\n",
      "Epoch: 5590/20000, Loss: 0.0000\n",
      "Epoch: 5600/20000, Loss: 0.0000\n",
      "Epoch: 5610/20000, Loss: 0.0000\n",
      "Epoch: 5620/20000, Loss: 0.0000\n",
      "Epoch: 5630/20000, Loss: 0.0000\n",
      "Epoch: 5640/20000, Loss: 0.0000\n",
      "Epoch: 5650/20000, Loss: 0.0001\n",
      "Epoch: 5660/20000, Loss: 0.0000\n",
      "Epoch: 5670/20000, Loss: 0.0000\n",
      "Epoch: 5680/20000, Loss: 0.0000\n",
      "Epoch: 5690/20000, Loss: 0.0000\n",
      "Epoch: 5700/20000, Loss: 0.0000\n",
      "Epoch: 5710/20000, Loss: 0.0000\n",
      "Epoch: 5720/20000, Loss: 0.0000\n",
      "Epoch: 5730/20000, Loss: 0.0000\n",
      "Epoch: 5740/20000, Loss: 0.0000\n",
      "Epoch: 5750/20000, Loss: 0.0000\n",
      "Epoch: 5760/20000, Loss: 0.0000\n",
      "Epoch: 5770/20000, Loss: 0.0000\n",
      "Epoch: 5780/20000, Loss: 0.0000\n",
      "Epoch: 5790/20000, Loss: 0.0001\n",
      "Epoch: 5800/20000, Loss: 0.0001\n",
      "Epoch: 5810/20000, Loss: 0.0000\n",
      "Epoch: 5820/20000, Loss: 0.0000\n",
      "Epoch: 5830/20000, Loss: 0.0000\n",
      "Epoch: 5840/20000, Loss: 0.0000\n",
      "Epoch: 5850/20000, Loss: 0.0000\n",
      "Epoch: 5860/20000, Loss: 0.0000\n",
      "Epoch: 5870/20000, Loss: 0.0000\n",
      "Epoch: 5880/20000, Loss: 0.0000\n",
      "Epoch: 5890/20000, Loss: 0.0000\n",
      "Epoch: 5900/20000, Loss: 0.0000\n",
      "Epoch: 5910/20000, Loss: 0.0000\n",
      "Epoch: 5920/20000, Loss: 0.0000\n",
      "Epoch: 5930/20000, Loss: 0.0000\n",
      "Epoch: 5940/20000, Loss: 0.0000\n",
      "Epoch: 5950/20000, Loss: 0.0001\n",
      "Epoch: 5960/20000, Loss: 0.0000\n",
      "Epoch: 5970/20000, Loss: 0.0000\n",
      "Epoch: 5980/20000, Loss: 0.0000\n",
      "Epoch: 5990/20000, Loss: 0.0000\n",
      "Epoch: 6000/20000, Loss: 0.0000\n",
      "Epoch: 6010/20000, Loss: 0.0000\n",
      "Epoch: 6020/20000, Loss: 0.0000\n",
      "Epoch: 6030/20000, Loss: 0.0000\n",
      "Epoch: 6040/20000, Loss: 0.0000\n",
      "Epoch: 6050/20000, Loss: 0.0000\n",
      "Epoch: 6060/20000, Loss: 0.0000\n",
      "Epoch: 6070/20000, Loss: 0.0000\n",
      "Epoch: 6080/20000, Loss: 0.0000\n",
      "Epoch: 6090/20000, Loss: 0.0000\n",
      "Epoch: 6100/20000, Loss: 0.0001\n",
      "Epoch: 6110/20000, Loss: 0.0000\n",
      "Epoch: 6120/20000, Loss: 0.0000\n",
      "Epoch: 6130/20000, Loss: 0.0000\n",
      "Epoch: 6140/20000, Loss: 0.0000\n",
      "Epoch: 6150/20000, Loss: 0.0000\n",
      "Epoch: 6160/20000, Loss: 0.0000\n",
      "Epoch: 6170/20000, Loss: 0.0000\n",
      "Epoch: 6180/20000, Loss: 0.0000\n",
      "Epoch: 6190/20000, Loss: 0.0000\n",
      "Epoch: 6200/20000, Loss: 0.0000\n",
      "Epoch: 6210/20000, Loss: 0.0000\n",
      "Epoch: 6220/20000, Loss: 0.0000\n",
      "Epoch: 6230/20000, Loss: 0.0000\n",
      "Epoch: 6240/20000, Loss: 0.0000\n",
      "Epoch: 6250/20000, Loss: 0.0000\n",
      "Epoch: 6260/20000, Loss: 0.0001\n",
      "Epoch: 6270/20000, Loss: 0.0000\n",
      "Epoch: 6280/20000, Loss: 0.0000\n",
      "Epoch: 6290/20000, Loss: 0.0000\n",
      "Epoch: 6300/20000, Loss: 0.0000\n",
      "Epoch: 6310/20000, Loss: 0.0000\n",
      "Epoch: 6320/20000, Loss: 0.0000\n",
      "Epoch: 6330/20000, Loss: 0.0000\n",
      "Epoch: 6340/20000, Loss: 0.0000\n",
      "Epoch: 6350/20000, Loss: 0.0000\n",
      "Epoch: 6360/20000, Loss: 0.0000\n",
      "Epoch: 6370/20000, Loss: 0.0000\n",
      "Epoch: 6380/20000, Loss: 0.0000\n",
      "Epoch: 6390/20000, Loss: 0.0000\n",
      "Epoch: 6400/20000, Loss: 0.0001\n",
      "Epoch: 6410/20000, Loss: 0.0000\n",
      "Epoch: 6420/20000, Loss: 0.0000\n",
      "Epoch: 6430/20000, Loss: 0.0000\n",
      "Epoch: 6440/20000, Loss: 0.0000\n",
      "Epoch: 6450/20000, Loss: 0.0000\n",
      "Epoch: 6460/20000, Loss: 0.0000\n",
      "Epoch: 6470/20000, Loss: 0.0000\n",
      "Epoch: 6480/20000, Loss: 0.0000\n",
      "Epoch: 6490/20000, Loss: 0.0000\n",
      "Epoch: 6500/20000, Loss: 0.0000\n",
      "Epoch: 6510/20000, Loss: 0.0000\n",
      "Epoch: 6520/20000, Loss: 0.0000\n",
      "Epoch: 6530/20000, Loss: 0.0002\n",
      "Epoch: 6540/20000, Loss: 0.0000\n",
      "Epoch: 6550/20000, Loss: 0.0000\n",
      "Epoch: 6560/20000, Loss: 0.0000\n",
      "Epoch: 6570/20000, Loss: 0.0000\n",
      "Epoch: 6580/20000, Loss: 0.0000\n",
      "Epoch: 6590/20000, Loss: 0.0000\n",
      "Epoch: 6600/20000, Loss: 0.0000\n",
      "Epoch: 6610/20000, Loss: 0.0000\n",
      "Epoch: 6620/20000, Loss: 0.0000\n",
      "Epoch: 6630/20000, Loss: 0.0000\n",
      "Epoch: 6640/20000, Loss: 0.0000\n",
      "Epoch: 6650/20000, Loss: 0.0000\n",
      "Epoch: 6660/20000, Loss: 0.0000\n",
      "Epoch: 6670/20000, Loss: 0.0000\n",
      "Epoch: 6680/20000, Loss: 0.0000\n",
      "Epoch: 6690/20000, Loss: 0.0000\n",
      "Epoch: 6700/20000, Loss: 0.0000\n",
      "Epoch: 6710/20000, Loss: 0.0000\n",
      "Epoch: 6720/20000, Loss: 0.0000\n",
      "Epoch: 6730/20000, Loss: 0.0000\n",
      "Epoch: 6740/20000, Loss: 0.0000\n",
      "Epoch: 6750/20000, Loss: 0.0000\n",
      "Epoch: 6760/20000, Loss: 0.0000\n",
      "Epoch: 6770/20000, Loss: 0.0000\n",
      "Epoch: 6780/20000, Loss: 0.0000\n",
      "Epoch: 6790/20000, Loss: 0.0002\n",
      "Epoch: 6800/20000, Loss: 0.0001\n",
      "Epoch: 6810/20000, Loss: 0.0000\n",
      "Epoch: 6820/20000, Loss: 0.0000\n",
      "Epoch: 6830/20000, Loss: 0.0000\n",
      "Epoch: 6840/20000, Loss: 0.0000\n",
      "Epoch: 6850/20000, Loss: 0.0000\n",
      "Epoch: 6860/20000, Loss: 0.0000\n",
      "Epoch: 6870/20000, Loss: 0.0000\n",
      "Epoch: 6880/20000, Loss: 0.0000\n",
      "Epoch: 6890/20000, Loss: 0.0000\n",
      "Epoch: 6900/20000, Loss: 0.0001\n",
      "Epoch: 6910/20000, Loss: 0.0000\n",
      "Epoch: 6920/20000, Loss: 0.0000\n",
      "Epoch: 6930/20000, Loss: 0.0000\n",
      "Epoch: 6940/20000, Loss: 0.0000\n",
      "Epoch: 6950/20000, Loss: 0.0000\n",
      "Epoch: 6960/20000, Loss: 0.0000\n",
      "Epoch: 6970/20000, Loss: 0.0000\n",
      "Epoch: 6980/20000, Loss: 0.0001\n",
      "Epoch: 6990/20000, Loss: 0.0000\n",
      "Epoch: 7000/20000, Loss: 0.0000\n",
      "Epoch: 7010/20000, Loss: 0.0000\n",
      "Epoch: 7020/20000, Loss: 0.0000\n",
      "Epoch: 7030/20000, Loss: 0.0000\n",
      "Epoch: 7040/20000, Loss: 0.0000\n",
      "Epoch: 7050/20000, Loss: 0.0000\n",
      "Epoch: 7060/20000, Loss: 0.0000\n",
      "Epoch: 7070/20000, Loss: 0.0000\n",
      "Epoch: 7080/20000, Loss: 0.0000\n",
      "Epoch: 7090/20000, Loss: 0.0000\n",
      "Epoch: 7100/20000, Loss: 0.0000\n",
      "Epoch: 7110/20000, Loss: 0.0000\n",
      "Epoch: 7120/20000, Loss: 0.0000\n",
      "Epoch: 7130/20000, Loss: 0.0000\n",
      "Epoch: 7140/20000, Loss: 0.0000\n",
      "Epoch: 7150/20000, Loss: 0.0000\n",
      "Epoch: 7160/20000, Loss: 0.0000\n",
      "Epoch: 7170/20000, Loss: 0.0000\n",
      "Epoch: 7180/20000, Loss: 0.0000\n",
      "Epoch: 7190/20000, Loss: 0.0001\n",
      "Epoch: 7200/20000, Loss: 0.0000\n",
      "Epoch: 7210/20000, Loss: 0.0000\n",
      "Epoch: 7220/20000, Loss: 0.0000\n",
      "Epoch: 7230/20000, Loss: 0.0000\n",
      "Epoch: 7240/20000, Loss: 0.0000\n",
      "Epoch: 7250/20000, Loss: 0.0000\n",
      "Epoch: 7260/20000, Loss: 0.0000\n",
      "Epoch: 7270/20000, Loss: 0.0000\n",
      "Epoch: 7280/20000, Loss: 0.0000\n",
      "Epoch: 7290/20000, Loss: 0.0000\n",
      "Epoch: 7300/20000, Loss: 0.0000\n",
      "Epoch: 7310/20000, Loss: 0.0000\n",
      "Epoch: 7320/20000, Loss: 0.0000\n",
      "Epoch: 7330/20000, Loss: 0.0000\n",
      "Epoch: 7340/20000, Loss: 0.0000\n",
      "Epoch: 7350/20000, Loss: 0.0000\n",
      "Epoch: 7360/20000, Loss: 0.0000\n",
      "Epoch: 7370/20000, Loss: 0.0000\n",
      "Epoch: 7380/20000, Loss: 0.0000\n",
      "Epoch: 7390/20000, Loss: 0.0000\n",
      "Epoch: 7400/20000, Loss: 0.0000\n",
      "Epoch: 7410/20000, Loss: 0.0000\n",
      "Epoch: 7420/20000, Loss: 0.0000\n",
      "Epoch: 7430/20000, Loss: 0.0000\n",
      "Epoch: 7440/20000, Loss: 0.0000\n",
      "Epoch: 7450/20000, Loss: 0.0001\n",
      "Epoch: 7460/20000, Loss: 0.0000\n",
      "Epoch: 7470/20000, Loss: 0.0000\n",
      "Epoch: 7480/20000, Loss: 0.0000\n",
      "Epoch: 7490/20000, Loss: 0.0000\n",
      "Epoch: 7500/20000, Loss: 0.0000\n",
      "Epoch: 7510/20000, Loss: 0.0000\n",
      "Epoch: 7520/20000, Loss: 0.0000\n",
      "Epoch: 7530/20000, Loss: 0.0000\n",
      "Epoch: 7540/20000, Loss: 0.0000\n",
      "Epoch: 7550/20000, Loss: 0.0001\n",
      "Epoch: 7560/20000, Loss: 0.0000\n",
      "Epoch: 7570/20000, Loss: 0.0000\n",
      "Epoch: 7580/20000, Loss: 0.0000\n",
      "Epoch: 7590/20000, Loss: 0.0000\n",
      "Epoch: 7600/20000, Loss: 0.0000\n",
      "Epoch: 7610/20000, Loss: 0.0000\n",
      "Epoch: 7620/20000, Loss: 0.0000\n",
      "Epoch: 7630/20000, Loss: 0.0000\n",
      "Epoch: 7640/20000, Loss: 0.0000\n",
      "Epoch: 7650/20000, Loss: 0.0000\n",
      "Epoch: 7660/20000, Loss: 0.0000\n",
      "Epoch: 7670/20000, Loss: 0.0001\n",
      "Epoch: 7680/20000, Loss: 0.0000\n",
      "Epoch: 7690/20000, Loss: 0.0000\n",
      "Epoch: 7700/20000, Loss: 0.0000\n",
      "Epoch: 7710/20000, Loss: 0.0000\n",
      "Epoch: 7720/20000, Loss: 0.0000\n",
      "Epoch: 7730/20000, Loss: 0.0000\n",
      "Epoch: 7740/20000, Loss: 0.0000\n",
      "Epoch: 7750/20000, Loss: 0.0000\n",
      "Epoch: 7760/20000, Loss: 0.0000\n",
      "Epoch: 7770/20000, Loss: 0.0000\n",
      "Epoch: 7780/20000, Loss: 0.0000\n",
      "Epoch: 7790/20000, Loss: 0.0000\n",
      "Epoch: 7800/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 7810/20000, Loss: 0.0000\n",
      "Epoch: 7820/20000, Loss: 0.0000\n",
      "Epoch: 7830/20000, Loss: 0.0001\n",
      "Epoch: 7840/20000, Loss: 0.0000\n",
      "Epoch: 7850/20000, Loss: 0.0000\n",
      "Epoch: 7860/20000, Loss: 0.0000\n",
      "Epoch: 7870/20000, Loss: 0.0000\n",
      "Epoch: 7880/20000, Loss: 0.0000\n",
      "Epoch: 7890/20000, Loss: 0.0000\n",
      "Epoch: 7900/20000, Loss: 0.0000\n",
      "Epoch: 7910/20000, Loss: 0.0000\n",
      "Epoch: 7920/20000, Loss: 0.0000\n",
      "Epoch: 7930/20000, Loss: 0.0000\n",
      "Epoch: 7940/20000, Loss: 0.0000\n",
      "Epoch: 7950/20000, Loss: 0.0000\n",
      "Epoch: 7960/20000, Loss: 0.0000\n",
      "Epoch: 7970/20000, Loss: 0.0000\n",
      "Epoch: 7980/20000, Loss: 0.0000\n",
      "Epoch: 7990/20000, Loss: 0.0000\n",
      "Epoch: 8000/20000, Loss: 0.0000\n",
      "Epoch: 8010/20000, Loss: 0.0000\n",
      "Epoch: 8020/20000, Loss: 0.0000\n",
      "Epoch: 8030/20000, Loss: 0.0000\n",
      "Epoch: 8040/20000, Loss: 0.0000\n",
      "Epoch: 8050/20000, Loss: 0.0000\n",
      "Epoch: 8060/20000, Loss: 0.0000\n",
      "Epoch: 8070/20000, Loss: 0.0000\n",
      "Epoch: 8080/20000, Loss: 0.0000\n",
      "Epoch: 8090/20000, Loss: 0.0000\n",
      "Epoch: 8100/20000, Loss: 0.0000\n",
      "Epoch: 8110/20000, Loss: 0.0000\n",
      "Epoch: 8120/20000, Loss: 0.0000\n",
      "Epoch: 8130/20000, Loss: 0.0000\n",
      "Epoch: 8140/20000, Loss: 0.0000\n",
      "Epoch: 8150/20000, Loss: 0.0000\n",
      "Epoch: 8160/20000, Loss: 0.0000\n",
      "Epoch: 8170/20000, Loss: 0.0000\n",
      "Epoch: 8180/20000, Loss: 0.0000\n",
      "Epoch: 8190/20000, Loss: 0.0000\n",
      "Epoch: 8200/20000, Loss: 0.0000\n",
      "Epoch: 8210/20000, Loss: 0.0000\n",
      "Epoch: 8220/20000, Loss: 0.0000\n",
      "Epoch: 8230/20000, Loss: 0.0000\n",
      "Epoch: 8240/20000, Loss: 0.0000\n",
      "Epoch: 8250/20000, Loss: 0.0001\n",
      "Epoch: 8260/20000, Loss: 0.0000\n",
      "Epoch: 8270/20000, Loss: 0.0000\n",
      "Epoch: 8280/20000, Loss: 0.0000\n",
      "Epoch: 8290/20000, Loss: 0.0000\n",
      "Epoch: 8300/20000, Loss: 0.0000\n",
      "Epoch: 8310/20000, Loss: 0.0000\n",
      "Epoch: 8320/20000, Loss: 0.0000\n",
      "Epoch: 8330/20000, Loss: 0.0000\n",
      "Epoch: 8340/20000, Loss: 0.0000\n",
      "Epoch: 8350/20000, Loss: 0.0000\n",
      "Epoch: 8360/20000, Loss: 0.0001\n",
      "Epoch: 8370/20000, Loss: 0.0000\n",
      "Epoch: 8380/20000, Loss: 0.0000\n",
      "Epoch: 8390/20000, Loss: 0.0000\n",
      "Epoch: 8400/20000, Loss: 0.0000\n",
      "Epoch: 8410/20000, Loss: 0.0000\n",
      "Epoch: 8420/20000, Loss: 0.0000\n",
      "Epoch: 8430/20000, Loss: 0.0000\n",
      "Epoch: 8440/20000, Loss: 0.0000\n",
      "Epoch: 8450/20000, Loss: 0.0000\n",
      "Epoch: 8460/20000, Loss: 0.0000\n",
      "Epoch: 8470/20000, Loss: 0.0000\n",
      "Epoch: 8480/20000, Loss: 0.0000\n",
      "Epoch: 8490/20000, Loss: 0.0000\n",
      "Epoch: 8500/20000, Loss: 0.0000\n",
      "Epoch: 8510/20000, Loss: 0.0000\n",
      "Epoch: 8520/20000, Loss: 0.0000\n",
      "Epoch: 8530/20000, Loss: 0.0000\n",
      "Epoch: 8540/20000, Loss: 0.0000\n",
      "Epoch: 8550/20000, Loss: 0.0000\n",
      "Epoch: 8560/20000, Loss: 0.0000\n",
      "Epoch: 8570/20000, Loss: 0.0000\n",
      "Epoch: 8580/20000, Loss: 0.0000\n",
      "Epoch: 8590/20000, Loss: 0.0000\n",
      "Epoch: 8600/20000, Loss: 0.0000\n",
      "Epoch: 8610/20000, Loss: 0.0000\n",
      "Epoch: 8620/20000, Loss: 0.0000\n",
      "Epoch: 8630/20000, Loss: 0.0000\n",
      "Epoch: 8640/20000, Loss: 0.0000\n",
      "Epoch: 8650/20000, Loss: 0.0000\n",
      "Epoch: 8660/20000, Loss: 0.0000\n",
      "Epoch: 8670/20000, Loss: 0.0000\n",
      "Epoch: 8680/20000, Loss: 0.0000\n",
      "Epoch: 8690/20000, Loss: 0.0000\n",
      "Epoch: 8700/20000, Loss: 0.0000\n",
      "Epoch: 8710/20000, Loss: 0.0001\n",
      "Epoch: 8720/20000, Loss: 0.0000\n",
      "Epoch: 8730/20000, Loss: 0.0000\n",
      "Epoch: 8740/20000, Loss: 0.0000\n",
      "Epoch: 8750/20000, Loss: 0.0000\n",
      "Epoch: 8760/20000, Loss: 0.0000\n",
      "Epoch: 8770/20000, Loss: 0.0000\n",
      "Epoch: 8780/20000, Loss: 0.0000\n",
      "Epoch: 8790/20000, Loss: 0.0000\n",
      "Epoch: 8800/20000, Loss: 0.0000\n",
      "Epoch: 8810/20000, Loss: 0.0000\n",
      "Epoch: 8820/20000, Loss: 0.0000\n",
      "Epoch: 8830/20000, Loss: 0.0000\n",
      "Epoch: 8840/20000, Loss: 0.0000\n",
      "Epoch: 8850/20000, Loss: 0.0000\n",
      "Epoch: 8860/20000, Loss: 0.0000\n",
      "Epoch: 8870/20000, Loss: 0.0000\n",
      "Epoch: 8880/20000, Loss: 0.0000\n",
      "Epoch: 8890/20000, Loss: 0.0000\n",
      "Epoch: 8900/20000, Loss: 0.0000\n",
      "Epoch: 8910/20000, Loss: 0.0000\n",
      "Epoch: 8920/20000, Loss: 0.0000\n",
      "Epoch: 8930/20000, Loss: 0.0000\n",
      "Epoch: 8940/20000, Loss: 0.0000\n",
      "Epoch: 8950/20000, Loss: 0.0000\n",
      "Epoch: 8960/20000, Loss: 0.0000\n",
      "Epoch: 8970/20000, Loss: 0.0000\n",
      "Epoch: 8980/20000, Loss: 0.0000\n",
      "Epoch: 8990/20000, Loss: 0.0000\n",
      "Epoch: 9000/20000, Loss: 0.0000\n",
      "Epoch: 9010/20000, Loss: 0.0000\n",
      "Epoch: 9020/20000, Loss: 0.0000\n",
      "Epoch: 9030/20000, Loss: 0.0000\n",
      "Epoch: 9040/20000, Loss: 0.0000\n",
      "Epoch: 9050/20000, Loss: 0.0000\n",
      "Epoch: 9060/20000, Loss: 0.0001\n",
      "Epoch: 9070/20000, Loss: 0.0000\n",
      "Epoch: 9080/20000, Loss: 0.0000\n",
      "Epoch: 9090/20000, Loss: 0.0000\n",
      "Epoch: 9100/20000, Loss: 0.0000\n",
      "Epoch: 9110/20000, Loss: 0.0000\n",
      "Epoch: 9120/20000, Loss: 0.0000\n",
      "Epoch: 9130/20000, Loss: 0.0000\n",
      "Epoch: 9140/20000, Loss: 0.0000\n",
      "Epoch: 9150/20000, Loss: 0.0000\n",
      "Epoch: 9160/20000, Loss: 0.0000\n",
      "Epoch: 9170/20000, Loss: 0.0001\n",
      "Epoch: 9180/20000, Loss: 0.0000\n",
      "Epoch: 9190/20000, Loss: 0.0000\n",
      "Epoch: 9200/20000, Loss: 0.0000\n",
      "Epoch: 9210/20000, Loss: 0.0000\n",
      "Epoch: 9220/20000, Loss: 0.0000\n",
      "Epoch: 9230/20000, Loss: 0.0000\n",
      "Epoch: 9240/20000, Loss: 0.0000\n",
      "Epoch: 9250/20000, Loss: 0.0000\n",
      "Epoch: 9260/20000, Loss: 0.0000\n",
      "Epoch: 9270/20000, Loss: 0.0000\n",
      "Epoch: 9280/20000, Loss: 0.0001\n",
      "Epoch: 9290/20000, Loss: 0.0000\n",
      "Epoch: 9300/20000, Loss: 0.0000\n",
      "Epoch: 9310/20000, Loss: 0.0000\n",
      "Epoch: 9320/20000, Loss: 0.0000\n",
      "Epoch: 9330/20000, Loss: 0.0000\n",
      "Epoch: 9340/20000, Loss: 0.0000\n",
      "Epoch: 9350/20000, Loss: 0.0000\n",
      "Epoch: 9360/20000, Loss: 0.0001\n",
      "Epoch: 9370/20000, Loss: 0.0000\n",
      "Epoch: 9380/20000, Loss: 0.0000\n",
      "Epoch: 9390/20000, Loss: 0.0000\n",
      "Epoch: 9400/20000, Loss: 0.0000\n",
      "Epoch: 9410/20000, Loss: 0.0000\n",
      "Epoch: 9420/20000, Loss: 0.0000\n",
      "Epoch: 9430/20000, Loss: 0.0000\n",
      "Epoch: 9440/20000, Loss: 0.0000\n",
      "Epoch: 9450/20000, Loss: 0.0000\n",
      "Epoch: 9460/20000, Loss: 0.0000\n",
      "Epoch: 9470/20000, Loss: 0.0000\n",
      "Epoch: 9480/20000, Loss: 0.0000\n",
      "Epoch: 9490/20000, Loss: 0.0000\n",
      "Epoch: 9500/20000, Loss: 0.0000\n",
      "Epoch: 9510/20000, Loss: 0.0000\n",
      "Epoch: 9520/20000, Loss: 0.0000\n",
      "Epoch: 9530/20000, Loss: 0.0000\n",
      "Epoch: 9540/20000, Loss: 0.0000\n",
      "Epoch: 9550/20000, Loss: 0.0000\n",
      "Epoch: 9560/20000, Loss: 0.0001\n",
      "Epoch: 9570/20000, Loss: 0.0000\n",
      "Epoch: 9580/20000, Loss: 0.0000\n",
      "Epoch: 9590/20000, Loss: 0.0000\n",
      "Epoch: 9600/20000, Loss: 0.0000\n",
      "Epoch: 9610/20000, Loss: 0.0000\n",
      "Epoch: 9620/20000, Loss: 0.0000\n",
      "Epoch: 9630/20000, Loss: 0.0000\n",
      "Epoch: 9640/20000, Loss: 0.0000\n",
      "Epoch: 9650/20000, Loss: 0.0000\n",
      "Epoch: 9660/20000, Loss: 0.0000\n",
      "Epoch: 9670/20000, Loss: 0.0001\n",
      "Epoch: 9680/20000, Loss: 0.0000\n",
      "Epoch: 9690/20000, Loss: 0.0000\n",
      "Epoch: 9700/20000, Loss: 0.0000\n",
      "Epoch: 9710/20000, Loss: 0.0000\n",
      "Epoch: 9720/20000, Loss: 0.0000\n",
      "Epoch: 9730/20000, Loss: 0.0000\n",
      "Epoch: 9740/20000, Loss: 0.0000\n",
      "Epoch: 9750/20000, Loss: 0.0000\n",
      "Epoch: 9760/20000, Loss: 0.0000\n",
      "Epoch: 9770/20000, Loss: 0.0000\n",
      "Epoch: 9780/20000, Loss: 0.0000\n",
      "Epoch: 9790/20000, Loss: 0.0000\n",
      "Epoch: 9800/20000, Loss: 0.0000\n",
      "Epoch: 9810/20000, Loss: 0.0000\n",
      "Epoch: 9820/20000, Loss: 0.0000\n",
      "Epoch: 9830/20000, Loss: 0.0000\n",
      "Epoch: 9840/20000, Loss: 0.0000\n",
      "Epoch: 9850/20000, Loss: 0.0000\n",
      "Epoch: 9860/20000, Loss: 0.0000\n",
      "Epoch: 9870/20000, Loss: 0.0000\n",
      "Epoch: 9880/20000, Loss: 0.0000\n",
      "Epoch: 9890/20000, Loss: 0.0000\n",
      "Epoch: 9900/20000, Loss: 0.0000\n",
      "Epoch: 9910/20000, Loss: 0.0000\n",
      "Epoch: 9920/20000, Loss: 0.0000\n",
      "Epoch: 9930/20000, Loss: 0.0000\n",
      "Epoch: 9940/20000, Loss: 0.0000\n",
      "Epoch: 9950/20000, Loss: 0.0001\n",
      "Epoch: 9960/20000, Loss: 0.0000\n",
      "Epoch: 9970/20000, Loss: 0.0000\n",
      "Epoch: 9980/20000, Loss: 0.0000\n",
      "Epoch: 9990/20000, Loss: 0.0000\n",
      "Epoch: 10000/20000, Loss: 0.0000\n",
      "Epoch: 10010/20000, Loss: 0.0000\n",
      "Epoch: 10020/20000, Loss: 0.0000\n",
      "Epoch: 10030/20000, Loss: 0.0000\n",
      "Epoch: 10040/20000, Loss: 0.0000\n",
      "Epoch: 10050/20000, Loss: 0.0000\n",
      "Epoch: 10060/20000, Loss: 0.0000\n",
      "Epoch: 10070/20000, Loss: 0.0000\n",
      "Epoch: 10080/20000, Loss: 0.0000\n",
      "Epoch: 10090/20000, Loss: 0.0000\n",
      "Epoch: 10100/20000, Loss: 0.0000\n",
      "Epoch: 10110/20000, Loss: 0.0000\n",
      "Epoch: 10120/20000, Loss: 0.0000\n",
      "Epoch: 10130/20000, Loss: 0.0000\n",
      "Epoch: 10140/20000, Loss: 0.0000\n",
      "Epoch: 10150/20000, Loss: 0.0000\n",
      "Epoch: 10160/20000, Loss: 0.0000\n",
      "Epoch: 10170/20000, Loss: 0.0000\n",
      "Epoch: 10180/20000, Loss: 0.0000\n",
      "Epoch: 10190/20000, Loss: 0.0000\n",
      "Epoch: 10200/20000, Loss: 0.0000\n",
      "Epoch: 10210/20000, Loss: 0.0000\n",
      "Epoch: 10220/20000, Loss: 0.0000\n",
      "Epoch: 10230/20000, Loss: 0.0000\n",
      "Epoch: 10240/20000, Loss: 0.0000\n",
      "Epoch: 10250/20000, Loss: 0.0000\n",
      "Epoch: 10260/20000, Loss: 0.0000\n",
      "Epoch: 10270/20000, Loss: 0.0000\n",
      "Epoch: 10280/20000, Loss: 0.0000\n",
      "Epoch: 10290/20000, Loss: 0.0000\n",
      "Epoch: 10300/20000, Loss: 0.0000\n",
      "Epoch: 10310/20000, Loss: 0.0000\n",
      "Epoch: 10320/20000, Loss: 0.0000\n",
      "Epoch: 10330/20000, Loss: 0.0000\n",
      "Epoch: 10340/20000, Loss: 0.0000\n",
      "Epoch: 10350/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10360/20000, Loss: 0.0000\n",
      "Epoch: 10370/20000, Loss: 0.0000\n",
      "Epoch: 10380/20000, Loss: 0.0001\n",
      "Epoch: 10390/20000, Loss: 0.0000\n",
      "Epoch: 10400/20000, Loss: 0.0000\n",
      "Epoch: 10410/20000, Loss: 0.0000\n",
      "Epoch: 10420/20000, Loss: 0.0000\n",
      "Epoch: 10430/20000, Loss: 0.0000\n",
      "Epoch: 10440/20000, Loss: 0.0000\n",
      "Epoch: 10450/20000, Loss: 0.0000\n",
      "Epoch: 10460/20000, Loss: 0.0000\n",
      "Epoch: 10470/20000, Loss: 0.0000\n",
      "Epoch: 10480/20000, Loss: 0.0000\n",
      "Epoch: 10490/20000, Loss: 0.0000\n",
      "Epoch: 10500/20000, Loss: 0.0000\n",
      "Epoch: 10510/20000, Loss: 0.0000\n",
      "Epoch: 10520/20000, Loss: 0.0000\n",
      "Epoch: 10530/20000, Loss: 0.0000\n",
      "Epoch: 10540/20000, Loss: 0.0000\n",
      "Epoch: 10550/20000, Loss: 0.0001\n",
      "Epoch: 10560/20000, Loss: 0.0000\n",
      "Epoch: 10570/20000, Loss: 0.0000\n",
      "Epoch: 10580/20000, Loss: 0.0000\n",
      "Epoch: 10590/20000, Loss: 0.0000\n",
      "Epoch: 10600/20000, Loss: 0.0000\n",
      "Epoch: 10610/20000, Loss: 0.0000\n",
      "Epoch: 10620/20000, Loss: 0.0000\n",
      "Epoch: 10630/20000, Loss: 0.0000\n",
      "Epoch: 10640/20000, Loss: 0.0000\n",
      "Epoch: 10650/20000, Loss: 0.0001\n",
      "Epoch: 10660/20000, Loss: 0.0000\n",
      "Epoch: 10670/20000, Loss: 0.0000\n",
      "Epoch: 10680/20000, Loss: 0.0000\n",
      "Epoch: 10690/20000, Loss: 0.0000\n",
      "Epoch: 10700/20000, Loss: 0.0000\n",
      "Epoch: 10710/20000, Loss: 0.0000\n",
      "Epoch: 10720/20000, Loss: 0.0000\n",
      "Epoch: 10730/20000, Loss: 0.0000\n",
      "Epoch: 10740/20000, Loss: 0.0000\n",
      "Epoch: 10750/20000, Loss: 0.0000\n",
      "Epoch: 10760/20000, Loss: 0.0000\n",
      "Epoch: 10770/20000, Loss: 0.0001\n",
      "Epoch: 10780/20000, Loss: 0.0000\n",
      "Epoch: 10790/20000, Loss: 0.0000\n",
      "Epoch: 10800/20000, Loss: 0.0000\n",
      "Epoch: 10810/20000, Loss: 0.0000\n",
      "Epoch: 10820/20000, Loss: 0.0000\n",
      "Epoch: 10830/20000, Loss: 0.0000\n",
      "Epoch: 10840/20000, Loss: 0.0000\n",
      "Epoch: 10850/20000, Loss: 0.0000\n",
      "Epoch: 10860/20000, Loss: 0.0000\n",
      "Epoch: 10870/20000, Loss: 0.0000\n",
      "Epoch: 10880/20000, Loss: 0.0001\n",
      "Epoch: 10890/20000, Loss: 0.0000\n",
      "Epoch: 10900/20000, Loss: 0.0000\n",
      "Epoch: 10910/20000, Loss: 0.0000\n",
      "Epoch: 10920/20000, Loss: 0.0000\n",
      "Epoch: 10930/20000, Loss: 0.0000\n",
      "Epoch: 10940/20000, Loss: 0.0000\n",
      "Epoch: 10950/20000, Loss: 0.0000\n",
      "Epoch: 10960/20000, Loss: 0.0000\n",
      "Epoch: 10970/20000, Loss: 0.0000\n",
      "Epoch: 10980/20000, Loss: 0.0000\n",
      "Epoch: 10990/20000, Loss: 0.0000\n",
      "Epoch: 11000/20000, Loss: 0.0001\n",
      "Epoch: 11010/20000, Loss: 0.0000\n",
      "Epoch: 11020/20000, Loss: 0.0000\n",
      "Epoch: 11030/20000, Loss: 0.0000\n",
      "Epoch: 11040/20000, Loss: 0.0000\n",
      "Epoch: 11050/20000, Loss: 0.0000\n",
      "Epoch: 11060/20000, Loss: 0.0000\n",
      "Epoch: 11070/20000, Loss: 0.0000\n",
      "Epoch: 11080/20000, Loss: 0.0000\n",
      "Epoch: 11090/20000, Loss: 0.0000\n",
      "Epoch: 11100/20000, Loss: 0.0000\n",
      "Epoch: 11110/20000, Loss: 0.0000\n",
      "Epoch: 11120/20000, Loss: 0.0000\n",
      "Epoch: 11130/20000, Loss: 0.0000\n",
      "Epoch: 11140/20000, Loss: 0.0000\n",
      "Epoch: 11150/20000, Loss: 0.0000\n",
      "Epoch: 11160/20000, Loss: 0.0001\n",
      "Epoch: 11170/20000, Loss: 0.0000\n",
      "Epoch: 11180/20000, Loss: 0.0000\n",
      "Epoch: 11190/20000, Loss: 0.0000\n",
      "Epoch: 11200/20000, Loss: 0.0000\n",
      "Epoch: 11210/20000, Loss: 0.0000\n",
      "Epoch: 11220/20000, Loss: 0.0000\n",
      "Epoch: 11230/20000, Loss: 0.0000\n",
      "Epoch: 11240/20000, Loss: 0.0000\n",
      "Epoch: 11250/20000, Loss: 0.0000\n",
      "Epoch: 11260/20000, Loss: 0.0000\n",
      "Epoch: 11270/20000, Loss: 0.0000\n",
      "Epoch: 11280/20000, Loss: 0.0000\n",
      "Epoch: 11290/20000, Loss: 0.0000\n",
      "Epoch: 11300/20000, Loss: 0.0000\n",
      "Epoch: 11310/20000, Loss: 0.0000\n",
      "Epoch: 11320/20000, Loss: 0.0000\n",
      "Epoch: 11330/20000, Loss: 0.0000\n",
      "Epoch: 11340/20000, Loss: 0.0000\n",
      "Epoch: 11350/20000, Loss: 0.0000\n",
      "Epoch: 11360/20000, Loss: 0.0000\n",
      "Epoch: 11370/20000, Loss: 0.0000\n",
      "Epoch: 11380/20000, Loss: 0.0000\n",
      "Epoch: 11390/20000, Loss: 0.0000\n",
      "Epoch: 11400/20000, Loss: 0.0000\n",
      "Epoch: 11410/20000, Loss: 0.0000\n",
      "Epoch: 11420/20000, Loss: 0.0000\n",
      "Epoch: 11430/20000, Loss: 0.0000\n",
      "Epoch: 11440/20000, Loss: 0.0000\n",
      "Epoch: 11450/20000, Loss: 0.0000\n",
      "Epoch: 11460/20000, Loss: 0.0000\n",
      "Epoch: 11470/20000, Loss: 0.0000\n",
      "Epoch: 11480/20000, Loss: 0.0000\n",
      "Epoch: 11490/20000, Loss: 0.0000\n",
      "Epoch: 11500/20000, Loss: 0.0000\n",
      "Epoch: 11510/20000, Loss: 0.0000\n",
      "Epoch: 11520/20000, Loss: 0.0000\n",
      "Epoch: 11530/20000, Loss: 0.0000\n",
      "Epoch: 11540/20000, Loss: 0.0000\n",
      "Epoch: 11550/20000, Loss: 0.0000\n",
      "Epoch: 11560/20000, Loss: 0.0000\n",
      "Epoch: 11570/20000, Loss: 0.0000\n",
      "Epoch: 11580/20000, Loss: 0.0000\n",
      "Epoch: 11590/20000, Loss: 0.0000\n",
      "Epoch: 11600/20000, Loss: 0.0000\n",
      "Epoch: 11610/20000, Loss: 0.0000\n",
      "Epoch: 11620/20000, Loss: 0.0000\n",
      "Epoch: 11630/20000, Loss: 0.0000\n",
      "Epoch: 11640/20000, Loss: 0.0001\n",
      "Epoch: 11650/20000, Loss: 0.0000\n",
      "Epoch: 11660/20000, Loss: 0.0000\n",
      "Epoch: 11670/20000, Loss: 0.0000\n",
      "Epoch: 11680/20000, Loss: 0.0000\n",
      "Epoch: 11690/20000, Loss: 0.0000\n",
      "Epoch: 11700/20000, Loss: 0.0000\n",
      "Epoch: 11710/20000, Loss: 0.0000\n",
      "Epoch: 11720/20000, Loss: 0.0000\n",
      "Epoch: 11730/20000, Loss: 0.0000\n",
      "Epoch: 11740/20000, Loss: 0.0000\n",
      "Epoch: 11750/20000, Loss: 0.0000\n",
      "Epoch: 11760/20000, Loss: 0.0000\n",
      "Epoch: 11770/20000, Loss: 0.0000\n",
      "Epoch: 11780/20000, Loss: 0.0000\n",
      "Epoch: 11790/20000, Loss: 0.0000\n",
      "Epoch: 11800/20000, Loss: 0.0001\n",
      "Epoch: 11810/20000, Loss: 0.0000\n",
      "Epoch: 11820/20000, Loss: 0.0000\n",
      "Epoch: 11830/20000, Loss: 0.0000\n",
      "Epoch: 11840/20000, Loss: 0.0000\n",
      "Epoch: 11850/20000, Loss: 0.0000\n",
      "Epoch: 11860/20000, Loss: 0.0000\n",
      "Epoch: 11870/20000, Loss: 0.0000\n",
      "Epoch: 11880/20000, Loss: 0.0000\n",
      "Epoch: 11890/20000, Loss: 0.0000\n",
      "Epoch: 11900/20000, Loss: 0.0000\n",
      "Epoch: 11910/20000, Loss: 0.0000\n",
      "Epoch: 11920/20000, Loss: 0.0000\n",
      "Epoch: 11930/20000, Loss: 0.0000\n",
      "Epoch: 11940/20000, Loss: 0.0000\n",
      "Epoch: 11950/20000, Loss: 0.0000\n",
      "Epoch: 11960/20000, Loss: 0.0000\n",
      "Epoch: 11970/20000, Loss: 0.0000\n",
      "Epoch: 11980/20000, Loss: 0.0000\n",
      "Epoch: 11990/20000, Loss: 0.0000\n",
      "Epoch: 12000/20000, Loss: 0.0000\n",
      "Epoch: 12010/20000, Loss: 0.0000\n",
      "Epoch: 12020/20000, Loss: 0.0000\n",
      "Epoch: 12030/20000, Loss: 0.0000\n",
      "Epoch: 12040/20000, Loss: 0.0000\n",
      "Epoch: 12050/20000, Loss: 0.0000\n",
      "Epoch: 12060/20000, Loss: 0.0000\n",
      "Epoch: 12070/20000, Loss: 0.0001\n",
      "Epoch: 12080/20000, Loss: 0.0001\n",
      "Epoch: 12090/20000, Loss: 0.0000\n",
      "Epoch: 12100/20000, Loss: 0.0000\n",
      "Epoch: 12110/20000, Loss: 0.0000\n",
      "Epoch: 12120/20000, Loss: 0.0000\n",
      "Epoch: 12130/20000, Loss: 0.0000\n",
      "Epoch: 12140/20000, Loss: 0.0000\n",
      "Epoch: 12150/20000, Loss: 0.0000\n",
      "Epoch: 12160/20000, Loss: 0.0000\n",
      "Epoch: 12170/20000, Loss: 0.0000\n",
      "Epoch: 12180/20000, Loss: 0.0000\n",
      "Epoch: 12190/20000, Loss: 0.0000\n",
      "Epoch: 12200/20000, Loss: 0.0000\n",
      "Epoch: 12210/20000, Loss: 0.0000\n",
      "Epoch: 12220/20000, Loss: 0.0000\n",
      "Epoch: 12230/20000, Loss: 0.0000\n",
      "Epoch: 12240/20000, Loss: 0.0000\n",
      "Epoch: 12250/20000, Loss: 0.0000\n",
      "Epoch: 12260/20000, Loss: 0.0000\n",
      "Epoch: 12270/20000, Loss: 0.0000\n",
      "Epoch: 12280/20000, Loss: 0.0000\n",
      "Epoch: 12290/20000, Loss: 0.0001\n",
      "Epoch: 12300/20000, Loss: 0.0000\n",
      "Epoch: 12310/20000, Loss: 0.0000\n",
      "Epoch: 12320/20000, Loss: 0.0000\n",
      "Epoch: 12330/20000, Loss: 0.0000\n",
      "Epoch: 12340/20000, Loss: 0.0000\n",
      "Epoch: 12350/20000, Loss: 0.0000\n",
      "Epoch: 12360/20000, Loss: 0.0000\n",
      "Epoch: 12370/20000, Loss: 0.0000\n",
      "Epoch: 12380/20000, Loss: 0.0000\n",
      "Epoch: 12390/20000, Loss: 0.0001\n",
      "Epoch: 12400/20000, Loss: 0.0000\n",
      "Epoch: 12410/20000, Loss: 0.0000\n",
      "Epoch: 12420/20000, Loss: 0.0000\n",
      "Epoch: 12430/20000, Loss: 0.0000\n",
      "Epoch: 12440/20000, Loss: 0.0000\n",
      "Epoch: 12450/20000, Loss: 0.0000\n",
      "Epoch: 12460/20000, Loss: 0.0000\n",
      "Epoch: 12470/20000, Loss: 0.0001\n",
      "Epoch: 12480/20000, Loss: 0.0000\n",
      "Epoch: 12490/20000, Loss: 0.0000\n",
      "Epoch: 12500/20000, Loss: 0.0000\n",
      "Epoch: 12510/20000, Loss: 0.0000\n",
      "Epoch: 12520/20000, Loss: 0.0000\n",
      "Epoch: 12530/20000, Loss: 0.0000\n",
      "Epoch: 12540/20000, Loss: 0.0000\n",
      "Epoch: 12550/20000, Loss: 0.0000\n",
      "Epoch: 12560/20000, Loss: 0.0000\n",
      "Epoch: 12570/20000, Loss: 0.0000\n",
      "Epoch: 12580/20000, Loss: 0.0000\n",
      "Epoch: 12590/20000, Loss: 0.0000\n",
      "Epoch: 12600/20000, Loss: 0.0000\n",
      "Epoch: 12610/20000, Loss: 0.0000\n",
      "Epoch: 12620/20000, Loss: 0.0000\n",
      "Epoch: 12630/20000, Loss: 0.0000\n",
      "Epoch: 12640/20000, Loss: 0.0000\n",
      "Epoch: 12650/20000, Loss: 0.0000\n",
      "Epoch: 12660/20000, Loss: 0.0000\n",
      "Epoch: 12670/20000, Loss: 0.0000\n",
      "Epoch: 12680/20000, Loss: 0.0000\n",
      "Epoch: 12690/20000, Loss: 0.0000\n",
      "Epoch: 12700/20000, Loss: 0.0000\n",
      "Epoch: 12710/20000, Loss: 0.0000\n",
      "Epoch: 12720/20000, Loss: 0.0001\n",
      "Epoch: 12730/20000, Loss: 0.0000\n",
      "Epoch: 12740/20000, Loss: 0.0000\n",
      "Epoch: 12750/20000, Loss: 0.0000\n",
      "Epoch: 12760/20000, Loss: 0.0000\n",
      "Epoch: 12770/20000, Loss: 0.0000\n",
      "Epoch: 12780/20000, Loss: 0.0000\n",
      "Epoch: 12790/20000, Loss: 0.0000\n",
      "Epoch: 12800/20000, Loss: 0.0000\n",
      "Epoch: 12810/20000, Loss: 0.0000\n",
      "Epoch: 12820/20000, Loss: 0.0000\n",
      "Epoch: 12830/20000, Loss: 0.0000\n",
      "Epoch: 12840/20000, Loss: 0.0001\n",
      "Epoch: 12850/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 12860/20000, Loss: 0.0000\n",
      "Epoch: 12870/20000, Loss: 0.0000\n",
      "Epoch: 12880/20000, Loss: 0.0000\n",
      "Epoch: 12890/20000, Loss: 0.0000\n",
      "Epoch: 12900/20000, Loss: 0.0000\n",
      "Epoch: 12910/20000, Loss: 0.0000\n",
      "Epoch: 12920/20000, Loss: 0.0000\n",
      "Epoch: 12930/20000, Loss: 0.0000\n",
      "Epoch: 12940/20000, Loss: 0.0000\n",
      "Epoch: 12950/20000, Loss: 0.0000\n",
      "Epoch: 12960/20000, Loss: 0.0000\n",
      "Epoch: 12970/20000, Loss: 0.0000\n",
      "Epoch: 12980/20000, Loss: 0.0000\n",
      "Epoch: 12990/20000, Loss: 0.0000\n",
      "Epoch: 13000/20000, Loss: 0.0000\n",
      "Epoch: 13010/20000, Loss: 0.0000\n",
      "Epoch: 13020/20000, Loss: 0.0000\n",
      "Epoch: 13030/20000, Loss: 0.0000\n",
      "Epoch: 13040/20000, Loss: 0.0000\n",
      "Epoch: 13050/20000, Loss: 0.0000\n",
      "Epoch: 13060/20000, Loss: 0.0000\n",
      "Epoch: 13070/20000, Loss: 0.0001\n",
      "Epoch: 13080/20000, Loss: 0.0000\n",
      "Epoch: 13090/20000, Loss: 0.0000\n",
      "Epoch: 13100/20000, Loss: 0.0000\n",
      "Epoch: 13110/20000, Loss: 0.0000\n",
      "Epoch: 13120/20000, Loss: 0.0000\n",
      "Epoch: 13130/20000, Loss: 0.0000\n",
      "Epoch: 13140/20000, Loss: 0.0000\n",
      "Epoch: 13150/20000, Loss: 0.0000\n",
      "Epoch: 13160/20000, Loss: 0.0000\n",
      "Epoch: 13170/20000, Loss: 0.0000\n",
      "Epoch: 13180/20000, Loss: 0.0000\n",
      "Epoch: 13190/20000, Loss: 0.0000\n",
      "Epoch: 13200/20000, Loss: 0.0000\n",
      "Epoch: 13210/20000, Loss: 0.0000\n",
      "Epoch: 13220/20000, Loss: 0.0000\n",
      "Epoch: 13230/20000, Loss: 0.0000\n",
      "Epoch: 13240/20000, Loss: 0.0000\n",
      "Epoch: 13250/20000, Loss: 0.0000\n",
      "Epoch: 13260/20000, Loss: 0.0000\n",
      "Epoch: 13270/20000, Loss: 0.0000\n",
      "Epoch: 13280/20000, Loss: 0.0000\n",
      "Epoch: 13290/20000, Loss: 0.0000\n",
      "Epoch: 13300/20000, Loss: 0.0000\n",
      "Epoch: 13310/20000, Loss: 0.0000\n",
      "Epoch: 13320/20000, Loss: 0.0000\n",
      "Epoch: 13330/20000, Loss: 0.0000\n",
      "Epoch: 13340/20000, Loss: 0.0000\n",
      "Epoch: 13350/20000, Loss: 0.0000\n",
      "Epoch: 13360/20000, Loss: 0.0000\n",
      "Epoch: 13370/20000, Loss: 0.0000\n",
      "Epoch: 13380/20000, Loss: 0.0000\n",
      "Epoch: 13390/20000, Loss: 0.0000\n",
      "Epoch: 13400/20000, Loss: 0.0000\n",
      "Epoch: 13410/20000, Loss: 0.0000\n",
      "Epoch: 13420/20000, Loss: 0.0001\n",
      "Epoch: 13430/20000, Loss: 0.0000\n",
      "Epoch: 13440/20000, Loss: 0.0000\n",
      "Epoch: 13450/20000, Loss: 0.0000\n",
      "Epoch: 13460/20000, Loss: 0.0000\n",
      "Epoch: 13470/20000, Loss: 0.0000\n",
      "Epoch: 13480/20000, Loss: 0.0000\n",
      "Epoch: 13490/20000, Loss: 0.0000\n",
      "Epoch: 13500/20000, Loss: 0.0000\n",
      "Epoch: 13510/20000, Loss: 0.0000\n",
      "Epoch: 13520/20000, Loss: 0.0000\n",
      "Epoch: 13530/20000, Loss: 0.0000\n",
      "Epoch: 13540/20000, Loss: 0.0000\n",
      "Epoch: 13550/20000, Loss: 0.0000\n",
      "Epoch: 13560/20000, Loss: 0.0000\n",
      "Epoch: 13570/20000, Loss: 0.0000\n",
      "Epoch: 13580/20000, Loss: 0.0000\n",
      "Epoch: 13590/20000, Loss: 0.0000\n",
      "Epoch: 13600/20000, Loss: 0.0000\n",
      "Epoch: 13610/20000, Loss: 0.0001\n",
      "Epoch: 13620/20000, Loss: 0.0000\n",
      "Epoch: 13630/20000, Loss: 0.0000\n",
      "Epoch: 13640/20000, Loss: 0.0000\n",
      "Epoch: 13650/20000, Loss: 0.0000\n",
      "Epoch: 13660/20000, Loss: 0.0000\n",
      "Epoch: 13670/20000, Loss: 0.0000\n",
      "Epoch: 13680/20000, Loss: 0.0000\n",
      "Epoch: 13690/20000, Loss: 0.0000\n",
      "Epoch: 13700/20000, Loss: 0.0000\n",
      "Epoch: 13710/20000, Loss: 0.0000\n",
      "Epoch: 13720/20000, Loss: 0.0000\n",
      "Epoch: 13730/20000, Loss: 0.0000\n",
      "Epoch: 13740/20000, Loss: 0.0000\n",
      "Epoch: 13750/20000, Loss: 0.0000\n",
      "Epoch: 13760/20000, Loss: 0.0000\n",
      "Epoch: 13770/20000, Loss: 0.0000\n",
      "Epoch: 13780/20000, Loss: 0.0000\n",
      "Epoch: 13790/20000, Loss: 0.0000\n",
      "Epoch: 13800/20000, Loss: 0.0000\n",
      "Epoch: 13810/20000, Loss: 0.0000\n",
      "Epoch: 13820/20000, Loss: 0.0000\n",
      "Epoch: 13830/20000, Loss: 0.0000\n",
      "Epoch: 13840/20000, Loss: 0.0000\n",
      "Epoch: 13850/20000, Loss: 0.0000\n",
      "Epoch: 13860/20000, Loss: 0.0000\n",
      "Epoch: 13870/20000, Loss: 0.0000\n",
      "Epoch: 13880/20000, Loss: 0.0000\n",
      "Epoch: 13890/20000, Loss: 0.0000\n",
      "Epoch: 13900/20000, Loss: 0.0000\n",
      "Epoch: 13910/20000, Loss: 0.0000\n",
      "Epoch: 13920/20000, Loss: 0.0000\n",
      "Epoch: 13930/20000, Loss: 0.0000\n",
      "Epoch: 13940/20000, Loss: 0.0000\n",
      "Epoch: 13950/20000, Loss: 0.0000\n",
      "Epoch: 13960/20000, Loss: 0.0000\n",
      "Epoch: 13970/20000, Loss: 0.0000\n",
      "Epoch: 13980/20000, Loss: 0.0000\n",
      "Epoch: 13990/20000, Loss: 0.0000\n",
      "Epoch: 14000/20000, Loss: 0.0000\n",
      "Epoch: 14010/20000, Loss: 0.0000\n",
      "Epoch: 14020/20000, Loss: 0.0000\n",
      "Epoch: 14030/20000, Loss: 0.0000\n",
      "Epoch: 14040/20000, Loss: 0.0000\n",
      "Epoch: 14050/20000, Loss: 0.0000\n",
      "Epoch: 14060/20000, Loss: 0.0000\n",
      "Epoch: 14070/20000, Loss: 0.0000\n",
      "Epoch: 14080/20000, Loss: 0.0000\n",
      "Epoch: 14090/20000, Loss: 0.0000\n",
      "Epoch: 14100/20000, Loss: 0.0000\n",
      "Epoch: 14110/20000, Loss: 0.0000\n",
      "Epoch: 14120/20000, Loss: 0.0000\n",
      "Epoch: 14130/20000, Loss: 0.0000\n",
      "Epoch: 14140/20000, Loss: 0.0000\n",
      "Epoch: 14150/20000, Loss: 0.0000\n",
      "Epoch: 14160/20000, Loss: 0.0000\n",
      "Epoch: 14170/20000, Loss: 0.0000\n",
      "Epoch: 14180/20000, Loss: 0.0000\n",
      "Epoch: 14190/20000, Loss: 0.0000\n",
      "Epoch: 14200/20000, Loss: 0.0000\n",
      "Epoch: 14210/20000, Loss: 0.0000\n",
      "Epoch: 14220/20000, Loss: 0.0000\n",
      "Epoch: 14230/20000, Loss: 0.0000\n",
      "Epoch: 14240/20000, Loss: 0.0000\n",
      "Epoch: 14250/20000, Loss: 0.0000\n",
      "Epoch: 14260/20000, Loss: 0.0000\n",
      "Epoch: 14270/20000, Loss: 0.0000\n",
      "Epoch: 14280/20000, Loss: 0.0000\n",
      "Epoch: 14290/20000, Loss: 0.0000\n",
      "Epoch: 14300/20000, Loss: 0.0000\n",
      "Epoch: 14310/20000, Loss: 0.0000\n",
      "Epoch: 14320/20000, Loss: 0.0000\n",
      "Epoch: 14330/20000, Loss: 0.0000\n",
      "Epoch: 14340/20000, Loss: 0.0000\n",
      "Epoch: 14350/20000, Loss: 0.0000\n",
      "Epoch: 14360/20000, Loss: 0.0000\n",
      "Epoch: 14370/20000, Loss: 0.0000\n",
      "Epoch: 14380/20000, Loss: 0.0000\n",
      "Epoch: 14390/20000, Loss: 0.0000\n",
      "Epoch: 14400/20000, Loss: 0.0000\n",
      "Epoch: 14410/20000, Loss: 0.0000\n",
      "Epoch: 14420/20000, Loss: 0.0000\n",
      "Epoch: 14430/20000, Loss: 0.0000\n",
      "Epoch: 14440/20000, Loss: 0.0000\n",
      "Epoch: 14450/20000, Loss: 0.0000\n",
      "Epoch: 14460/20000, Loss: 0.0000\n",
      "Epoch: 14470/20000, Loss: 0.0000\n",
      "Epoch: 14480/20000, Loss: 0.0000\n",
      "Epoch: 14490/20000, Loss: 0.0000\n",
      "Epoch: 14500/20000, Loss: 0.0000\n",
      "Epoch: 14510/20000, Loss: 0.0000\n",
      "Epoch: 14520/20000, Loss: 0.0000\n",
      "Epoch: 14530/20000, Loss: 0.0000\n",
      "Epoch: 14540/20000, Loss: 0.0000\n",
      "Epoch: 14550/20000, Loss: 0.0000\n",
      "Epoch: 14560/20000, Loss: 0.0000\n",
      "Epoch: 14570/20000, Loss: 0.0000\n",
      "Epoch: 14580/20000, Loss: 0.0000\n",
      "Epoch: 14590/20000, Loss: 0.0000\n",
      "Epoch: 14600/20000, Loss: 0.0000\n",
      "Epoch: 14610/20000, Loss: 0.0000\n",
      "Epoch: 14620/20000, Loss: 0.0000\n",
      "Epoch: 14630/20000, Loss: 0.0000\n",
      "Epoch: 14640/20000, Loss: 0.0000\n",
      "Epoch: 14650/20000, Loss: 0.0000\n",
      "Epoch: 14660/20000, Loss: 0.0000\n",
      "Epoch: 14670/20000, Loss: 0.0000\n",
      "Epoch: 14680/20000, Loss: 0.0000\n",
      "Epoch: 14690/20000, Loss: 0.0000\n",
      "Epoch: 14700/20000, Loss: 0.0000\n",
      "Epoch: 14710/20000, Loss: 0.0000\n",
      "Epoch: 14720/20000, Loss: 0.0000\n",
      "Epoch: 14730/20000, Loss: 0.0000\n",
      "Epoch: 14740/20000, Loss: 0.0000\n",
      "Epoch: 14750/20000, Loss: 0.0000\n",
      "Epoch: 14760/20000, Loss: 0.0000\n",
      "Epoch: 14770/20000, Loss: 0.0000\n",
      "Epoch: 14780/20000, Loss: 0.0000\n",
      "Epoch: 14790/20000, Loss: 0.0000\n",
      "Epoch: 14800/20000, Loss: 0.0000\n",
      "Epoch: 14810/20000, Loss: 0.0000\n",
      "Epoch: 14820/20000, Loss: 0.0000\n",
      "Epoch: 14830/20000, Loss: 0.0000\n",
      "Epoch: 14840/20000, Loss: 0.0000\n",
      "Epoch: 14850/20000, Loss: 0.0000\n",
      "Epoch: 14860/20000, Loss: 0.0000\n",
      "Epoch: 14870/20000, Loss: 0.0000\n",
      "Epoch: 14880/20000, Loss: 0.0000\n",
      "Epoch: 14890/20000, Loss: 0.0000\n",
      "Epoch: 14900/20000, Loss: 0.0000\n",
      "Epoch: 14910/20000, Loss: 0.0000\n",
      "Epoch: 14920/20000, Loss: 0.0000\n",
      "Epoch: 14930/20000, Loss: 0.0000\n",
      "Epoch: 14940/20000, Loss: 0.0000\n",
      "Epoch: 14950/20000, Loss: 0.0000\n",
      "Epoch: 14960/20000, Loss: 0.0000\n",
      "Epoch: 14970/20000, Loss: 0.0000\n",
      "Epoch: 14980/20000, Loss: 0.0000\n",
      "Epoch: 14990/20000, Loss: 0.0000\n",
      "Epoch: 15000/20000, Loss: 0.0000\n",
      "Epoch: 15010/20000, Loss: 0.0000\n",
      "Epoch: 15020/20000, Loss: 0.0000\n",
      "Epoch: 15030/20000, Loss: 0.0000\n",
      "Epoch: 15040/20000, Loss: 0.0000\n",
      "Epoch: 15050/20000, Loss: 0.0000\n",
      "Epoch: 15060/20000, Loss: 0.0000\n",
      "Epoch: 15070/20000, Loss: 0.0001\n",
      "Epoch: 15080/20000, Loss: 0.0000\n",
      "Epoch: 15090/20000, Loss: 0.0000\n",
      "Epoch: 15100/20000, Loss: 0.0000\n",
      "Epoch: 15110/20000, Loss: 0.0000\n",
      "Epoch: 15120/20000, Loss: 0.0000\n",
      "Epoch: 15130/20000, Loss: 0.0000\n",
      "Epoch: 15140/20000, Loss: 0.0000\n",
      "Epoch: 15150/20000, Loss: 0.0000\n",
      "Epoch: 15160/20000, Loss: 0.0000\n",
      "Epoch: 15170/20000, Loss: 0.0000\n",
      "Epoch: 15180/20000, Loss: 0.0000\n",
      "Epoch: 15190/20000, Loss: 0.0000\n",
      "Epoch: 15200/20000, Loss: 0.0000\n",
      "Epoch: 15210/20000, Loss: 0.0000\n",
      "Epoch: 15220/20000, Loss: 0.0000\n",
      "Epoch: 15230/20000, Loss: 0.0000\n",
      "Epoch: 15240/20000, Loss: 0.0000\n",
      "Epoch: 15250/20000, Loss: 0.0000\n",
      "Epoch: 15260/20000, Loss: 0.0000\n",
      "Epoch: 15270/20000, Loss: 0.0000\n",
      "Epoch: 15280/20000, Loss: 0.0000\n",
      "Epoch: 15290/20000, Loss: 0.0000\n",
      "Epoch: 15300/20000, Loss: 0.0000\n",
      "Epoch: 15310/20000, Loss: 0.0000\n",
      "Epoch: 15320/20000, Loss: 0.0000\n",
      "Epoch: 15330/20000, Loss: 0.0000\n",
      "Epoch: 15340/20000, Loss: 0.0000\n",
      "Epoch: 15350/20000, Loss: 0.0000\n",
      "Epoch: 15360/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 15370/20000, Loss: 0.0000\n",
      "Epoch: 15380/20000, Loss: 0.0000\n",
      "Epoch: 15390/20000, Loss: 0.0000\n",
      "Epoch: 15400/20000, Loss: 0.0000\n",
      "Epoch: 15410/20000, Loss: 0.0000\n",
      "Epoch: 15420/20000, Loss: 0.0000\n",
      "Epoch: 15430/20000, Loss: 0.0000\n",
      "Epoch: 15440/20000, Loss: 0.0000\n",
      "Epoch: 15450/20000, Loss: 0.0000\n",
      "Epoch: 15460/20000, Loss: 0.0000\n",
      "Epoch: 15470/20000, Loss: 0.0000\n",
      "Epoch: 15480/20000, Loss: 0.0000\n",
      "Epoch: 15490/20000, Loss: 0.0000\n",
      "Epoch: 15500/20000, Loss: 0.0000\n",
      "Epoch: 15510/20000, Loss: 0.0000\n",
      "Epoch: 15520/20000, Loss: 0.0000\n",
      "Epoch: 15530/20000, Loss: 0.0000\n",
      "Epoch: 15540/20000, Loss: 0.0000\n",
      "Epoch: 15550/20000, Loss: 0.0000\n",
      "Epoch: 15560/20000, Loss: 0.0000\n",
      "Epoch: 15570/20000, Loss: 0.0000\n",
      "Epoch: 15580/20000, Loss: 0.0000\n",
      "Epoch: 15590/20000, Loss: 0.0000\n",
      "Epoch: 15600/20000, Loss: 0.0000\n",
      "Epoch: 15610/20000, Loss: 0.0000\n",
      "Epoch: 15620/20000, Loss: 0.0000\n",
      "Epoch: 15630/20000, Loss: 0.0000\n",
      "Epoch: 15640/20000, Loss: 0.0000\n",
      "Epoch: 15650/20000, Loss: 0.0000\n",
      "Epoch: 15660/20000, Loss: 0.0000\n",
      "Epoch: 15670/20000, Loss: 0.0000\n",
      "Epoch: 15680/20000, Loss: 0.0000\n",
      "Epoch: 15690/20000, Loss: 0.0000\n",
      "Epoch: 15700/20000, Loss: 0.0000\n",
      "Epoch: 15710/20000, Loss: 0.0000\n",
      "Epoch: 15720/20000, Loss: 0.0000\n",
      "Epoch: 15730/20000, Loss: 0.0000\n",
      "Epoch: 15740/20000, Loss: 0.0000\n",
      "Epoch: 15750/20000, Loss: 0.0000\n",
      "Epoch: 15760/20000, Loss: 0.0000\n",
      "Epoch: 15770/20000, Loss: 0.0000\n",
      "Epoch: 15780/20000, Loss: 0.0001\n",
      "Epoch: 15790/20000, Loss: 0.0000\n",
      "Epoch: 15800/20000, Loss: 0.0000\n",
      "Epoch: 15810/20000, Loss: 0.0000\n",
      "Epoch: 15820/20000, Loss: 0.0000\n",
      "Epoch: 15830/20000, Loss: 0.0000\n",
      "Epoch: 15840/20000, Loss: 0.0000\n",
      "Epoch: 15850/20000, Loss: 0.0000\n",
      "Epoch: 15860/20000, Loss: 0.0000\n",
      "Epoch: 15870/20000, Loss: 0.0000\n",
      "Epoch: 15880/20000, Loss: 0.0000\n",
      "Epoch: 15890/20000, Loss: 0.0000\n",
      "Epoch: 15900/20000, Loss: 0.0000\n",
      "Epoch: 15910/20000, Loss: 0.0000\n",
      "Epoch: 15920/20000, Loss: 0.0000\n",
      "Epoch: 15930/20000, Loss: 0.0000\n",
      "Epoch: 15940/20000, Loss: 0.0000\n",
      "Epoch: 15950/20000, Loss: 0.0000\n",
      "Epoch: 15960/20000, Loss: 0.0000\n",
      "Epoch: 15970/20000, Loss: 0.0000\n",
      "Epoch: 15980/20000, Loss: 0.0000\n",
      "Epoch: 15990/20000, Loss: 0.0000\n",
      "Epoch: 16000/20000, Loss: 0.0000\n",
      "Epoch: 16010/20000, Loss: 0.0000\n",
      "Epoch: 16020/20000, Loss: 0.0000\n",
      "Epoch: 16030/20000, Loss: 0.0000\n",
      "Epoch: 16040/20000, Loss: 0.0000\n",
      "Epoch: 16050/20000, Loss: 0.0000\n",
      "Epoch: 16060/20000, Loss: 0.0000\n",
      "Epoch: 16070/20000, Loss: 0.0000\n",
      "Epoch: 16080/20000, Loss: 0.0000\n",
      "Epoch: 16090/20000, Loss: 0.0000\n",
      "Epoch: 16100/20000, Loss: 0.0000\n",
      "Epoch: 16110/20000, Loss: 0.0000\n",
      "Epoch: 16120/20000, Loss: 0.0000\n",
      "Epoch: 16130/20000, Loss: 0.0000\n",
      "Epoch: 16140/20000, Loss: 0.0000\n",
      "Epoch: 16150/20000, Loss: 0.0001\n",
      "Epoch: 16160/20000, Loss: 0.0000\n",
      "Epoch: 16170/20000, Loss: 0.0000\n",
      "Epoch: 16180/20000, Loss: 0.0000\n",
      "Epoch: 16190/20000, Loss: 0.0000\n",
      "Epoch: 16200/20000, Loss: 0.0000\n",
      "Epoch: 16210/20000, Loss: 0.0000\n",
      "Epoch: 16220/20000, Loss: 0.0000\n",
      "Epoch: 16230/20000, Loss: 0.0000\n",
      "Epoch: 16240/20000, Loss: 0.0000\n",
      "Epoch: 16250/20000, Loss: 0.0000\n",
      "Epoch: 16260/20000, Loss: 0.0000\n",
      "Epoch: 16270/20000, Loss: 0.0000\n",
      "Epoch: 16280/20000, Loss: 0.0000\n",
      "Epoch: 16290/20000, Loss: 0.0000\n",
      "Epoch: 16300/20000, Loss: 0.0000\n",
      "Epoch: 16310/20000, Loss: 0.0000\n",
      "Epoch: 16320/20000, Loss: 0.0000\n",
      "Epoch: 16330/20000, Loss: 0.0000\n",
      "Epoch: 16340/20000, Loss: 0.0000\n",
      "Epoch: 16350/20000, Loss: 0.0000\n",
      "Epoch: 16360/20000, Loss: 0.0000\n",
      "Epoch: 16370/20000, Loss: 0.0000\n",
      "Epoch: 16380/20000, Loss: 0.0000\n",
      "Epoch: 16390/20000, Loss: 0.0000\n",
      "Epoch: 16400/20000, Loss: 0.0000\n",
      "Epoch: 16410/20000, Loss: 0.0000\n",
      "Epoch: 16420/20000, Loss: 0.0000\n",
      "Epoch: 16430/20000, Loss: 0.0000\n",
      "Epoch: 16440/20000, Loss: 0.0000\n",
      "Epoch: 16450/20000, Loss: 0.0000\n",
      "Epoch: 16460/20000, Loss: 0.0000\n",
      "Epoch: 16470/20000, Loss: 0.0000\n",
      "Epoch: 16480/20000, Loss: 0.0000\n",
      "Epoch: 16490/20000, Loss: 0.0000\n",
      "Epoch: 16500/20000, Loss: 0.0000\n",
      "Epoch: 16510/20000, Loss: 0.0000\n",
      "Epoch: 16520/20000, Loss: 0.0000\n",
      "Epoch: 16530/20000, Loss: 0.0000\n",
      "Epoch: 16540/20000, Loss: 0.0000\n",
      "Epoch: 16550/20000, Loss: 0.0000\n",
      "Epoch: 16560/20000, Loss: 0.0000\n",
      "Epoch: 16570/20000, Loss: 0.0000\n",
      "Epoch: 16580/20000, Loss: 0.0000\n",
      "Epoch: 16590/20000, Loss: 0.0000\n",
      "Epoch: 16600/20000, Loss: 0.0000\n",
      "Epoch: 16610/20000, Loss: 0.0000\n",
      "Epoch: 16620/20000, Loss: 0.0000\n",
      "Epoch: 16630/20000, Loss: 0.0000\n",
      "Epoch: 16640/20000, Loss: 0.0000\n",
      "Epoch: 16650/20000, Loss: 0.0000\n",
      "Epoch: 16660/20000, Loss: 0.0000\n",
      "Epoch: 16670/20000, Loss: 0.0000\n",
      "Epoch: 16680/20000, Loss: 0.0000\n",
      "Epoch: 16690/20000, Loss: 0.0000\n",
      "Epoch: 16700/20000, Loss: 0.0000\n",
      "Epoch: 16710/20000, Loss: 0.0000\n",
      "Epoch: 16720/20000, Loss: 0.0000\n",
      "Epoch: 16730/20000, Loss: 0.0000\n",
      "Epoch: 16740/20000, Loss: 0.0000\n",
      "Epoch: 16750/20000, Loss: 0.0000\n",
      "Epoch: 16760/20000, Loss: 0.0000\n",
      "Epoch: 16770/20000, Loss: 0.0000\n",
      "Epoch: 16780/20000, Loss: 0.0000\n",
      "Epoch: 16790/20000, Loss: 0.0000\n",
      "Epoch: 16800/20000, Loss: 0.0000\n",
      "Epoch: 16810/20000, Loss: 0.0000\n",
      "Epoch: 16820/20000, Loss: 0.0000\n",
      "Epoch: 16830/20000, Loss: 0.0000\n",
      "Epoch: 16840/20000, Loss: 0.0000\n",
      "Epoch: 16850/20000, Loss: 0.0000\n",
      "Epoch: 16860/20000, Loss: 0.0000\n",
      "Epoch: 16870/20000, Loss: 0.0000\n",
      "Epoch: 16880/20000, Loss: 0.0000\n",
      "Epoch: 16890/20000, Loss: 0.0000\n",
      "Epoch: 16900/20000, Loss: 0.0000\n",
      "Epoch: 16910/20000, Loss: 0.0000\n",
      "Epoch: 16920/20000, Loss: 0.0000\n",
      "Epoch: 16930/20000, Loss: 0.0000\n",
      "Epoch: 16940/20000, Loss: 0.0000\n",
      "Epoch: 16950/20000, Loss: 0.0000\n",
      "Epoch: 16960/20000, Loss: 0.0000\n",
      "Epoch: 16970/20000, Loss: 0.0000\n",
      "Epoch: 16980/20000, Loss: 0.0000\n",
      "Epoch: 16990/20000, Loss: 0.0000\n",
      "Epoch: 17000/20000, Loss: 0.0000\n",
      "Epoch: 17010/20000, Loss: 0.0000\n",
      "Epoch: 17020/20000, Loss: 0.0000\n",
      "Epoch: 17030/20000, Loss: 0.0000\n",
      "Epoch: 17040/20000, Loss: 0.0000\n",
      "Epoch: 17050/20000, Loss: 0.0000\n",
      "Epoch: 17060/20000, Loss: 0.0000\n",
      "Epoch: 17070/20000, Loss: 0.0000\n",
      "Epoch: 17080/20000, Loss: 0.0000\n",
      "Epoch: 17090/20000, Loss: 0.0000\n",
      "Epoch: 17100/20000, Loss: 0.0000\n",
      "Epoch: 17110/20000, Loss: 0.0000\n",
      "Epoch: 17120/20000, Loss: 0.0000\n",
      "Epoch: 17130/20000, Loss: 0.0000\n",
      "Epoch: 17140/20000, Loss: 0.0000\n",
      "Epoch: 17150/20000, Loss: 0.0000\n",
      "Epoch: 17160/20000, Loss: 0.0000\n",
      "Epoch: 17170/20000, Loss: 0.0000\n",
      "Epoch: 17180/20000, Loss: 0.0000\n",
      "Epoch: 17190/20000, Loss: 0.0000\n",
      "Epoch: 17200/20000, Loss: 0.0000\n",
      "Epoch: 17210/20000, Loss: 0.0000\n",
      "Epoch: 17220/20000, Loss: 0.0000\n",
      "Epoch: 17230/20000, Loss: 0.0000\n",
      "Epoch: 17240/20000, Loss: 0.0001\n",
      "Epoch: 17250/20000, Loss: 0.0000\n",
      "Epoch: 17260/20000, Loss: 0.0000\n",
      "Epoch: 17270/20000, Loss: 0.0000\n",
      "Epoch: 17280/20000, Loss: 0.0000\n",
      "Epoch: 17290/20000, Loss: 0.0000\n",
      "Epoch: 17300/20000, Loss: 0.0000\n",
      "Epoch: 17310/20000, Loss: 0.0000\n",
      "Epoch: 17320/20000, Loss: 0.0000\n",
      "Epoch: 17330/20000, Loss: 0.0000\n",
      "Epoch: 17340/20000, Loss: 0.0000\n",
      "Epoch: 17350/20000, Loss: 0.0000\n",
      "Epoch: 17360/20000, Loss: 0.0000\n",
      "Epoch: 17370/20000, Loss: 0.0000\n",
      "Epoch: 17380/20000, Loss: 0.0000\n",
      "Epoch: 17390/20000, Loss: 0.0000\n",
      "Epoch: 17400/20000, Loss: 0.0000\n",
      "Epoch: 17410/20000, Loss: 0.0000\n",
      "Epoch: 17420/20000, Loss: 0.0000\n",
      "Epoch: 17430/20000, Loss: 0.0000\n",
      "Epoch: 17440/20000, Loss: 0.0000\n",
      "Epoch: 17450/20000, Loss: 0.0000\n",
      "Epoch: 17460/20000, Loss: 0.0000\n",
      "Epoch: 17470/20000, Loss: 0.0000\n",
      "Epoch: 17480/20000, Loss: 0.0000\n",
      "Epoch: 17490/20000, Loss: 0.0000\n",
      "Epoch: 17500/20000, Loss: 0.0000\n",
      "Epoch: 17510/20000, Loss: 0.0000\n",
      "Epoch: 17520/20000, Loss: 0.0000\n",
      "Epoch: 17530/20000, Loss: 0.0000\n",
      "Epoch: 17540/20000, Loss: 0.0000\n",
      "Epoch: 17550/20000, Loss: 0.0000\n",
      "Epoch: 17560/20000, Loss: 0.0000\n",
      "Epoch: 17570/20000, Loss: 0.0000\n",
      "Epoch: 17580/20000, Loss: 0.0000\n",
      "Epoch: 17590/20000, Loss: 0.0000\n",
      "Epoch: 17600/20000, Loss: 0.0000\n",
      "Epoch: 17610/20000, Loss: 0.0000\n",
      "Epoch: 17620/20000, Loss: 0.0000\n",
      "Epoch: 17630/20000, Loss: 0.0000\n",
      "Epoch: 17640/20000, Loss: 0.0000\n",
      "Epoch: 17650/20000, Loss: 0.0000\n",
      "Epoch: 17660/20000, Loss: 0.0000\n",
      "Epoch: 17670/20000, Loss: 0.0000\n",
      "Epoch: 17680/20000, Loss: 0.0000\n",
      "Epoch: 17690/20000, Loss: 0.0000\n",
      "Epoch: 17700/20000, Loss: 0.0000\n",
      "Epoch: 17710/20000, Loss: 0.0000\n",
      "Epoch: 17720/20000, Loss: 0.0001\n",
      "Epoch: 17730/20000, Loss: 0.0000\n",
      "Epoch: 17740/20000, Loss: 0.0000\n",
      "Epoch: 17750/20000, Loss: 0.0000\n",
      "Epoch: 17760/20000, Loss: 0.0000\n",
      "Epoch: 17770/20000, Loss: 0.0000\n",
      "Epoch: 17780/20000, Loss: 0.0000\n",
      "Epoch: 17790/20000, Loss: 0.0000\n",
      "Epoch: 17800/20000, Loss: 0.0000\n",
      "Epoch: 17810/20000, Loss: 0.0000\n",
      "Epoch: 17820/20000, Loss: 0.0000\n",
      "Epoch: 17830/20000, Loss: 0.0000\n",
      "Epoch: 17840/20000, Loss: 0.0000\n",
      "Epoch: 17850/20000, Loss: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 17860/20000, Loss: 0.0000\n",
      "Epoch: 17870/20000, Loss: 0.0000\n",
      "Epoch: 17880/20000, Loss: 0.0000\n",
      "Epoch: 17890/20000, Loss: 0.0000\n",
      "Epoch: 17900/20000, Loss: 0.0000\n",
      "Epoch: 17910/20000, Loss: 0.0000\n",
      "Epoch: 17920/20000, Loss: 0.0000\n",
      "Epoch: 17930/20000, Loss: 0.0000\n",
      "Epoch: 17940/20000, Loss: 0.0001\n",
      "Epoch: 17950/20000, Loss: 0.0001\n",
      "Epoch: 17960/20000, Loss: 0.0000\n",
      "Epoch: 17970/20000, Loss: 0.0000\n",
      "Epoch: 17980/20000, Loss: 0.0000\n",
      "Epoch: 17990/20000, Loss: 0.0000\n",
      "Epoch: 18000/20000, Loss: 0.0000\n",
      "Epoch: 18010/20000, Loss: 0.0000\n",
      "Epoch: 18020/20000, Loss: 0.0000\n",
      "Epoch: 18030/20000, Loss: 0.0000\n",
      "Epoch: 18040/20000, Loss: 0.0000\n",
      "Epoch: 18050/20000, Loss: 0.0000\n",
      "Epoch: 18060/20000, Loss: 0.0000\n",
      "Epoch: 18070/20000, Loss: 0.0000\n",
      "Epoch: 18080/20000, Loss: 0.0000\n",
      "Epoch: 18090/20000, Loss: 0.0000\n",
      "Epoch: 18100/20000, Loss: 0.0000\n",
      "Epoch: 18110/20000, Loss: 0.0000\n",
      "Epoch: 18120/20000, Loss: 0.0000\n",
      "Epoch: 18130/20000, Loss: 0.0000\n",
      "Epoch: 18140/20000, Loss: 0.0000\n",
      "Epoch: 18150/20000, Loss: 0.0000\n",
      "Epoch: 18160/20000, Loss: 0.0000\n",
      "Epoch: 18170/20000, Loss: 0.0000\n",
      "Epoch: 18180/20000, Loss: 0.0000\n",
      "Epoch: 18190/20000, Loss: 0.0000\n",
      "Epoch: 18200/20000, Loss: 0.0000\n",
      "Epoch: 18210/20000, Loss: 0.0000\n",
      "Epoch: 18220/20000, Loss: 0.0000\n",
      "Epoch: 18230/20000, Loss: 0.0000\n",
      "Epoch: 18240/20000, Loss: 0.0000\n",
      "Epoch: 18250/20000, Loss: 0.0000\n",
      "Epoch: 18260/20000, Loss: 0.0000\n",
      "Epoch: 18270/20000, Loss: 0.0000\n",
      "Epoch: 18280/20000, Loss: 0.0000\n",
      "Epoch: 18290/20000, Loss: 0.0000\n",
      "Epoch: 18300/20000, Loss: 0.0000\n",
      "Epoch: 18310/20000, Loss: 0.0000\n",
      "Epoch: 18320/20000, Loss: 0.0000\n",
      "Epoch: 18330/20000, Loss: 0.0000\n",
      "Epoch: 18340/20000, Loss: 0.0000\n",
      "Epoch: 18350/20000, Loss: 0.0000\n",
      "Epoch: 18360/20000, Loss: 0.0000\n",
      "Epoch: 18370/20000, Loss: 0.0000\n",
      "Epoch: 18380/20000, Loss: 0.0000\n",
      "Epoch: 18390/20000, Loss: 0.0001\n",
      "Epoch: 18400/20000, Loss: 0.0000\n",
      "Epoch: 18410/20000, Loss: 0.0000\n",
      "Epoch: 18420/20000, Loss: 0.0000\n",
      "Epoch: 18430/20000, Loss: 0.0000\n",
      "Epoch: 18440/20000, Loss: 0.0000\n",
      "Epoch: 18450/20000, Loss: 0.0000\n",
      "Epoch: 18460/20000, Loss: 0.0000\n",
      "Epoch: 18470/20000, Loss: 0.0000\n",
      "Epoch: 18480/20000, Loss: 0.0000\n",
      "Epoch: 18490/20000, Loss: 0.0000\n",
      "Epoch: 18500/20000, Loss: 0.0000\n",
      "Epoch: 18510/20000, Loss: 0.0000\n",
      "Epoch: 18520/20000, Loss: 0.0000\n",
      "Epoch: 18530/20000, Loss: 0.0000\n",
      "Epoch: 18540/20000, Loss: 0.0000\n",
      "Epoch: 18550/20000, Loss: 0.0000\n",
      "Epoch: 18560/20000, Loss: 0.0000\n",
      "Epoch: 18570/20000, Loss: 0.0000\n",
      "Epoch: 18580/20000, Loss: 0.0000\n",
      "Epoch: 18590/20000, Loss: 0.0000\n",
      "Epoch: 18600/20000, Loss: 0.0000\n",
      "Epoch: 18610/20000, Loss: 0.0000\n",
      "Epoch: 18620/20000, Loss: 0.0000\n",
      "Epoch: 18630/20000, Loss: 0.0000\n",
      "Epoch: 18640/20000, Loss: 0.0000\n",
      "Epoch: 18650/20000, Loss: 0.0000\n",
      "Epoch: 18660/20000, Loss: 0.0000\n",
      "Epoch: 18670/20000, Loss: 0.0000\n",
      "Epoch: 18680/20000, Loss: 0.0000\n",
      "Epoch: 18690/20000, Loss: 0.0000\n",
      "Epoch: 18700/20000, Loss: 0.0000\n",
      "Epoch: 18710/20000, Loss: 0.0000\n",
      "Epoch: 18720/20000, Loss: 0.0000\n",
      "Epoch: 18730/20000, Loss: 0.0001\n",
      "Epoch: 18740/20000, Loss: 0.0000\n",
      "Epoch: 18750/20000, Loss: 0.0000\n",
      "Epoch: 18760/20000, Loss: 0.0000\n",
      "Epoch: 18770/20000, Loss: 0.0000\n",
      "Epoch: 18780/20000, Loss: 0.0000\n",
      "Epoch: 18790/20000, Loss: 0.0000\n",
      "Epoch: 18800/20000, Loss: 0.0000\n",
      "Epoch: 18810/20000, Loss: 0.0000\n",
      "Epoch: 18820/20000, Loss: 0.0000\n",
      "Epoch: 18830/20000, Loss: 0.0000\n",
      "Epoch: 18840/20000, Loss: 0.0000\n",
      "Epoch: 18850/20000, Loss: 0.0000\n",
      "Epoch: 18860/20000, Loss: 0.0000\n",
      "Epoch: 18870/20000, Loss: 0.0000\n",
      "Epoch: 18880/20000, Loss: 0.0000\n",
      "Epoch: 18890/20000, Loss: 0.0000\n",
      "Epoch: 18900/20000, Loss: 0.0000\n",
      "Epoch: 18910/20000, Loss: 0.0000\n",
      "Epoch: 18920/20000, Loss: 0.0000\n",
      "Epoch: 18930/20000, Loss: 0.0000\n",
      "Epoch: 18940/20000, Loss: 0.0000\n",
      "Epoch: 18950/20000, Loss: 0.0000\n",
      "Epoch: 18960/20000, Loss: 0.0000\n",
      "Epoch: 18970/20000, Loss: 0.0000\n",
      "Epoch: 18980/20000, Loss: 0.0000\n",
      "Epoch: 18990/20000, Loss: 0.0000\n",
      "Epoch: 19000/20000, Loss: 0.0000\n",
      "Epoch: 19010/20000, Loss: 0.0000\n",
      "Epoch: 19020/20000, Loss: 0.0000\n",
      "Epoch: 19030/20000, Loss: 0.0000\n",
      "Epoch: 19040/20000, Loss: 0.0000\n",
      "Epoch: 19050/20000, Loss: 0.0000\n",
      "Epoch: 19060/20000, Loss: 0.0000\n",
      "Epoch: 19070/20000, Loss: 0.0000\n",
      "Epoch: 19080/20000, Loss: 0.0000\n",
      "Epoch: 19090/20000, Loss: 0.0000\n",
      "Epoch: 19100/20000, Loss: 0.0000\n",
      "Epoch: 19110/20000, Loss: 0.0000\n",
      "Epoch: 19120/20000, Loss: 0.0000\n",
      "Epoch: 19130/20000, Loss: 0.0000\n",
      "Epoch: 19140/20000, Loss: 0.0000\n",
      "Epoch: 19150/20000, Loss: 0.0000\n",
      "Epoch: 19160/20000, Loss: 0.0000\n",
      "Epoch: 19170/20000, Loss: 0.0000\n",
      "Epoch: 19180/20000, Loss: 0.0000\n",
      "Epoch: 19190/20000, Loss: 0.0001\n",
      "Epoch: 19200/20000, Loss: 0.0000\n",
      "Epoch: 19210/20000, Loss: 0.0000\n",
      "Epoch: 19220/20000, Loss: 0.0000\n",
      "Epoch: 19230/20000, Loss: 0.0000\n",
      "Epoch: 19240/20000, Loss: 0.0000\n",
      "Epoch: 19250/20000, Loss: 0.0000\n",
      "Epoch: 19260/20000, Loss: 0.0000\n",
      "Epoch: 19270/20000, Loss: 0.0000\n",
      "Epoch: 19280/20000, Loss: 0.0000\n",
      "Epoch: 19290/20000, Loss: 0.0000\n",
      "Epoch: 19300/20000, Loss: 0.0000\n",
      "Epoch: 19310/20000, Loss: 0.0000\n",
      "Epoch: 19320/20000, Loss: 0.0000\n",
      "Epoch: 19330/20000, Loss: 0.0000\n",
      "Epoch: 19340/20000, Loss: 0.0000\n",
      "Epoch: 19350/20000, Loss: 0.0000\n",
      "Epoch: 19360/20000, Loss: 0.0000\n",
      "Epoch: 19370/20000, Loss: 0.0000\n",
      "Epoch: 19380/20000, Loss: 0.0000\n",
      "Epoch: 19390/20000, Loss: 0.0000\n",
      "Epoch: 19400/20000, Loss: 0.0000\n",
      "Epoch: 19410/20000, Loss: 0.0000\n",
      "Epoch: 19420/20000, Loss: 0.0000\n",
      "Epoch: 19430/20000, Loss: 0.0000\n",
      "Epoch: 19440/20000, Loss: 0.0000\n",
      "Epoch: 19450/20000, Loss: 0.0000\n",
      "Epoch: 19460/20000, Loss: 0.0000\n",
      "Epoch: 19470/20000, Loss: 0.0000\n",
      "Epoch: 19480/20000, Loss: 0.0000\n",
      "Epoch: 19490/20000, Loss: 0.0000\n",
      "Epoch: 19500/20000, Loss: 0.0000\n",
      "Epoch: 19510/20000, Loss: 0.0000\n",
      "Epoch: 19520/20000, Loss: 0.0000\n",
      "Epoch: 19530/20000, Loss: 0.0000\n",
      "Epoch: 19540/20000, Loss: 0.0000\n",
      "Epoch: 19550/20000, Loss: 0.0000\n",
      "Epoch: 19560/20000, Loss: 0.0000\n",
      "Epoch: 19570/20000, Loss: 0.0000\n",
      "Epoch: 19580/20000, Loss: 0.0000\n",
      "Epoch: 19590/20000, Loss: 0.0000\n",
      "Epoch: 19600/20000, Loss: 0.0000\n",
      "Epoch: 19610/20000, Loss: 0.0000\n",
      "Epoch: 19620/20000, Loss: 0.0000\n",
      "Epoch: 19630/20000, Loss: 0.0000\n",
      "Epoch: 19640/20000, Loss: 0.0000\n",
      "Epoch: 19650/20000, Loss: 0.0000\n",
      "Epoch: 19660/20000, Loss: 0.0000\n",
      "Epoch: 19670/20000, Loss: 0.0000\n",
      "Epoch: 19680/20000, Loss: 0.0000\n",
      "Epoch: 19690/20000, Loss: 0.0000\n",
      "Epoch: 19700/20000, Loss: 0.0000\n",
      "Epoch: 19710/20000, Loss: 0.0000\n",
      "Epoch: 19720/20000, Loss: 0.0000\n",
      "Epoch: 19730/20000, Loss: 0.0000\n",
      "Epoch: 19740/20000, Loss: 0.0000\n",
      "Epoch: 19750/20000, Loss: 0.0000\n",
      "Epoch: 19760/20000, Loss: 0.0000\n",
      "Epoch: 19770/20000, Loss: 0.0000\n",
      "Epoch: 19780/20000, Loss: 0.0000\n",
      "Epoch: 19790/20000, Loss: 0.0000\n",
      "Epoch: 19800/20000, Loss: 0.0000\n",
      "Epoch: 19810/20000, Loss: 0.0000\n",
      "Epoch: 19820/20000, Loss: 0.0000\n",
      "Epoch: 19830/20000, Loss: 0.0000\n",
      "Epoch: 19840/20000, Loss: 0.0000\n",
      "Epoch: 19850/20000, Loss: 0.0000\n",
      "Epoch: 19860/20000, Loss: 0.0000\n",
      "Epoch: 19870/20000, Loss: 0.0000\n",
      "Epoch: 19880/20000, Loss: 0.0000\n",
      "Epoch: 19890/20000, Loss: 0.0000\n",
      "Epoch: 19900/20000, Loss: 0.0000\n",
      "Epoch: 19910/20000, Loss: 0.0000\n",
      "Epoch: 19920/20000, Loss: 0.0000\n",
      "Epoch: 19930/20000, Loss: 0.0000\n",
      "Epoch: 19940/20000, Loss: 0.0000\n",
      "Epoch: 19950/20000, Loss: 0.0000\n",
      "Epoch: 19960/20000, Loss: 0.0000\n",
      "Epoch: 19970/20000, Loss: 0.0000\n",
      "Epoch: 19980/20000, Loss: 0.0000\n",
      "Epoch: 19990/20000, Loss: 0.0000\n",
      "Epoch: 20000/20000, Loss: 0.0000\n"
     ]
    }
   ],
   "source": [
    "# Create LSTM instance\n",
    "lstm = LSTM(input_size, hidden_size, output_size)\n",
    "\n",
    "# Loss and optimizer\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = torch.optim.Adam(lstm.parameters(), lr=0.01)\n",
    "\n",
    "# Training loop\n",
    "for epoch in range(num_epochs):\n",
    "    # Set initial hidden state and cell state\n",
    "    hidden = torch.zeros(1, batch_size, hidden_size)\n",
    "    cell = torch.zeros(1, batch_size, hidden_size)\n",
    "\n",
    "    # Forward pass\n",
    "    output, (hidden, cell) = lstm(input_tensor, (hidden, cell))\n",
    "    loss = criterion(output, target_tensor)\n",
    "\n",
    "    # Backward and optimize\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    # Print progress\n",
    "    if (epoch+1) % 10 == 0:\n",
    "        print(f'Epoch: {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}')\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7d1d078b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 201])\n",
      "torch.Size([1, 20, 201])\n"
     ]
    }
   ],
   "source": [
    "print(test_tensor.shape)\n",
    "prediction_tensor = torch.zeros(1, 20, 201).float()\n",
    "print(prediction_tensor.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68654f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    hidden_pred = torch.zeros(1, batch_size, hidden_size)\n",
    "    cell_pred = torch.zeros(1, batch_size, hidden_size)\n",
    "    prediction, _ = lstm(test_tensor, (hidden_pred, cell_pred))\n",
    "    prediction = prediction.view(1, 1, 201).float()\n",
    "    prediction_tensor[:, 0, :] = prediction\n",
    "    for i in range(19):\n",
    "        hidden_pred = torch.zeros(1, batch_size, hidden_size)\n",
    "        cell_pred = torch.zeros(1, batch_size, hidden_size)\n",
    "        prediction, _ = lstm(test_tensor, (hidden_pred, cell_pred))\n",
    "        prediction = prediction.view(1, 1, 201).float()\n",
    "        prediction_tensor[:, i+1, :] = prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c82f19b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# exact\n",
    "u_test = u\n",
    "u_test_full = u_test[80:100, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ec50cb",
   "metadata": {},
   "source": [
    "### L2 error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "52347ae2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Relative Error Test:  0.05708208996536495 %\n"
     ]
    }
   ],
   "source": [
    "\n",
    "k1 = (prediction_tensor - u_test_full)**2\n",
    "u_test_full_tensor = torch.tensor(u_test_full**2)\n",
    "# Compute the relative L2 error norm (generalization error)\n",
    "relative_error_test = torch.mean(k1)/ torch.mean(u_test_full_tensor)\n",
    "\n",
    "print(\"Relative Error Test: \", relative_error_test.item(), \"%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f620459c",
   "metadata": {},
   "source": [
    "### Max absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d3e153c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.3996, dtype=torch.float64)\n"
     ]
    }
   ],
   "source": [
    "R_abs = torch.max(prediction-u_test_full)\n",
    "print(R_abs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7631fe31",
   "metadata": {},
   "source": [
    "### Explained variance score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "119f15f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Explained Variance Score: 0.9469701747177979\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "a = prediction\n",
    "b = u_test_full\n",
    "# Assuming 'a' is your predicted values (model's predictions) and 'b' is the true values (ground truth)\n",
    "# Make sure 'a' and 'b' are PyTorch tensors\n",
    "b = torch.tensor(b)\n",
    "# Calculate the mean of 'b'\n",
    "mean_b = torch.mean(b)\n",
    "\n",
    "# Calculate the Explained Variance Score\n",
    "numerator = torch.var(b - a)  # Variance of the differences between 'b' and 'a'\n",
    "denominator = torch.var(b)    # Variance of 'b'\n",
    "evs = 1 - numerator / denominator\n",
    "\n",
    "print(\"Explained Variance Score:\", evs.item())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e10c99fd",
   "metadata": {},
   "source": [
    "### Mean absolute error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14690b43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.1946, dtype=torch.float64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R_mean = torch.mean(torch.abs(prediction - u_test_full))\n",
    "R_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8d49950d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([20, 201])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_tensor = torch.squeeze(prediction_tensor)\n",
    "prediction_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0100ffa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_u = torch.squeeze(input_tensor)\n",
    "prediction_tensor = torch.squeeze(prediction_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6814f822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([20, 201])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_tensor = torch.squeeze(prediction_tensor)\n",
    "prediction_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "139d076b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([100, 201])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concatenated_tensor = torch.cat((conc_u, prediction_tensor), dim=0)\n",
    "\n",
    "concatenated_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8a1bce19",
   "metadata": {},
   "outputs": [],
   "source": [
    "t1 = np.linspace(0, 1 , 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55d65543",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Create the figure and axis objects with reduced width\n",
    "fig, ax = plt.subplots(figsize=(5, 5))  # You can adjust the width (7 inches) and height (5 inches) as needed\n",
    "\n",
    "# # Make sure the font is Times Roman\n",
    "# plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "# # Perform the prediction\n",
    "# with torch.no_grad():\n",
    "#     prediction = lem(test_tensor)\n",
    "\n",
    "final_time_output = prediction_tensor[-2, :]\n",
    "final_out = final_time_output.detach().numpy().reshape(-1, 1)\n",
    "final_true = u[-2, :].reshape(-1, 1)\n",
    "\n",
    "# Plot the data with red and blue lines, one with dotted and one with solid style\n",
    "ax.plot(x.T, final_out, color='red', linestyle='dotted', linewidth=12, label='Prediction')\n",
    "ax.plot(x.T, final_true, color='blue', linestyle='solid', linewidth=7, label='True')\n",
    "\n",
    "# Set the axis labels with bold font weight\n",
    "ax.set_xlabel(r\"${x}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "ax.set_ylabel(r\"${u(x, t)}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "\n",
    "# Set the title with bold font weight\n",
    "ax.set_title(r\"${t = 0.99}$\", fontsize=26, color='black', fontweight='bold')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 3\n",
    "ax.set_xticks([-1, 0, 1])\n",
    "ax.set_yticks([-1, 0, 1])\n",
    "\n",
    "# Set tick labels fontweight to bold and increase font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# # Set the fontweight for tick labels to bold\n",
    "# for tick in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "#     tick.set_weight('bold')\n",
    "\n",
    "# Set the spines linewidth to bold\n",
    "ax.spines['top'].set_linewidth(2)\n",
    "ax.spines['right'].set_linewidth(2)\n",
    "ax.spines['bottom'].set_linewidth(2)\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "\n",
    "# Increase font size for x and y axis numbers\n",
    "ax.tick_params(axis='both', which='major', labelsize=24)\n",
    "\n",
    "# Set the legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.savefig('LSTM_0.99_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "832a9243",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAF2CAYAAAB3QMMiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABGe0lEQVR4nO3dd5hU1fnA8e+7S9ldOosIKE2lo4CigL2LLSFo1Kg/YyyJJWpiYu89GhNbLNFYEksMduzGgiJIRKQoRVBYQCnSWcoW2PP749zdnd2de6feO3Nn3s/zzDM7c8/ce7a9886pYoxBKaVU+BRkugJKKaWSowFcKaVCSgO4UkqFlAZwpZQKKQ3gSikVUhrAlVIqpDSAKxUgERkjIu+IyEoRqRCRhSLykIjsmoZz9xKRv4rILBEpF5EqEVkhIu+KyK9EpNDldSIiA0TklyLyoIhMFZFKETEiUpFqvZR/RMeBq7ATkZ8D44AqoLUxpjrDVWpCRAT4B3CWS5FNwMnGmLeSPP8Y4BmglUex/wFHG2PWNXptL2CRy2sqjTFFydRJ+U8zcJUL9nLuZ2dj8HZcRX3wHgcMAToDPwXKgNbAf0Skb6InFpHewL+xwftH4HygP7AjsB/wglN0BPD3GKf7HngFmJhoPVTwNICrXFAbwKdntBYuRGRH4Grn4ZvAKcaYWcaYVcaY8cChwGZsEL8tiUucCxQBNcBxxphHjDHfGGN+NMZMNsacBLzslD1BRDo1ev0aYAzQ1RjT3RgzFvgwiXqogGkAV7lgT+c+KwM4cAb1TRvXmEbtlsaYRcBjzsOxItI5wfMPce4XGGOmupR5xrkvABq0txtjyo0xrxljViR4XZVhGsBVKIlIO6eTzQAdnacfqH3Oua3MZB0jHO/cLzDGzHQp86JzXwAcl+D5azsaazzKRB5bleD5VZbSAK7CanAcZb7yvRbxGebc/8+jzBfAdufrPT3KRfOlc99XRPZwKXOSc78A2+aucoAGcBVWU4A21LctL3ceR96OdXuxiBSKSOtUbvFUUkR2wrZtAyx0K2eMqXS+B4B+8Zw7wkPAaqAQeF1EThWRriJSLCKDReQx4FRgK3C+McYrU1ch0izTFVAqGcaY7cAmERngPDXDGLMpgVMcAHyUYjUkjjKRHYaxmi5+BHYGShOphDFmnYgcBLyEHX3ybKMiNdiRJbcaY75s/HoVXpqBq7Cr7cDL1g7MyHHZsSbFbHXu48ruIxlj5mBHkkyJcrgA+8bQK9HzquymAVyFloi0AOoy8ERea4yZYIyRVG5p/4ZSICI3AHOBXYBfAz2xmfwo7DjwvYGXROSyjFVSpZ0GcBVmA4HmztczMlgPL5sjvo41o7HYuU+kKQgRuRK4EZvBH2iMecwYs8QYs9YYM8UZB/6kU/xPIjI0kfOr7KVt4CrMhjr3m4BvE3mhsy5IccyCHuJsc18d8fUOMcrWHl8Tbx2cTyFXOA+fM8Z841L0euBX2KTtDLL3DU8lQAO4CrOhzv2sxpNj4hBUJ+Yy7BtMa6C364lEWgLdnIduQTiagUB752vXDkpjzPci8iN2+v4At3IqXLQJRYVZbQfmjExWwovzxlLbwTrCo+he2GGA4BGIo0jkU0TtG44OI8wRGsBVmNVO5pmV6AsD7sR83bnvKyK7u5Q50bmvAd5I4NyR099dJwCJSHfqm2iWJHB+lcU0gKtQcpZnrR1jvTGTdYnDv4AtztdNFqtylnP9tfPwZWPMj/Ge2FlHpXYp2FNFpI9L0Zsivn433vOr7KYBXIWS0zRRu9bJeSIyUETaOLMks2r9amPMSuoD9/Ei8ryI7C4iO4jIcdiV/1ph28qvafx6ETk4Yn2XM6Nc4lbnvgT4RETOFpHuItJBREaIyDhsBybY5pzxUa4xUERG1t6w48adQ/XPO7eWSf4oVJrphg4qtETkNuqn0kf6pzHmzICr4ymVDR1E5GDqO1x/ZYx5KkqZW7E/C6+mndnAMcaYJk0oIjIBOMjjtZF6G2PK4iyrfKQZuAqz64A/YhetipzlOCMjtfFgrLOBsdgmjFVAJbb54xFgaLK78TjnvxbYB3gcO4plM7DNuc4H2E0ehkcL3iq8NANXSqmQ0gxcKaVCSgO4UkqFlAZwpZQKKQ3gaRCxtZdSSqVNrNiia6GkkQZxpVSQ8iYDF5ETReQBEZkoIhudd7ZnYr9SKaWyUz5l4NdiFz/aBHyP3XoqrXRIplIqnez8L3d5k4EDvwf6Am2xkxqUUirU8iYDN8bUrf0c611NKaXCIJ8ycKWUyil5k4EnS0eWKKWylWbgSikVUpqBxxDPziuapSulMkEDuAqdmhqYPx++/hrmzIGVK6GqCqqroaQEunWzt0GDYOhQaKnbD8RkDJSVwbRpsHQpLFsGq1dDQQE0bw5t2kDfvvZnuvvu9rHKPA3gKjRmzoSHHoJXXoFVq+J7TcuWsOeecPzxcOqp0LOnv3UMk61b4Y03YNw4mDjRvhHGo7AQDj4YzjwTTj7ZBniVGXm5HnjEDifPGmNOT8P5DOhEHr8sWQIXXwyvvZb6uQ44AH73OxgzxmaX+WjJErjjDnj2WSgvT+1cPXrAPffA2LHpqZtqqHbIs1tTbp7+CauweOUVGDw4PcEbbKZ5wgk2K58wIT3nDIvVq+HSS6FPH3jkkdSDN9g3gxNOgDPOgMrK1M+nEpM3GbiIjAHGOA+7AEcBC4GJznOrjTF/TPLcmoH74PHH4de/tm3efvn1r+Evf4HWrf27RjZ46SX4zW9gzRr/rnH44fDqq9CqlX/XyDexMvB8CuA3Ajd4FFlsjOmV5Lk1gKfZ22/DscfazjW/DRgAr78Ou+7q/7WCtm0bXHYZ3HtvMNc74QR44QXQyc7poQE8ABrA02vZMhgyxH7kD0ppKbz7Luy1V3DX9FtFBZx0kn1zCtLf/gYXXhjsNXOVBvAAaABPr7Fjbdt3LHvuaW/9+9thbYWFsG6dfQOYNQs+/zyxdt727eH993MjiFdV2Y7at9+O/zUitr9h773taJ0dd7Q/06oqO8Tw66/hk09g82bv8xQVwTff2A5OlZpYARxjjN5SvAHG/ihVqqZNM8Y2nLjfjj/emLlzY59r2zZjJkww5pxzjGndOvZ5wZgddzSmrMz/79NPNTXG/PKX8X2/YMwBBxjz2GPGrF0b+9ybNhnz5z8bU1Lifc7zz/f928wLEbElauzRDDwNNANPnzFjvEec/OEP8Oc/R2lj3b4dJk2CBQts28s++8DIkVBcDNjM/M474a9/tRN+vOyxB0yZUvfS0Ln7btvuHcuIEXY44SGHJH6NqVNtp+XGjdGPt2gB330HO++c+LlVPc3ANQMPjQULvLO6I44wZvv2KC+cMMGYoUObvqBtW2OefbZB0ZkzjenfvyZmVvrb3wbzPafbF18Y06yZ9/fWrJkx997r8rNMwLhx3te54gpjPwbV1KTle8tHEbEleuxxO6A3DeBBu/1292BQUGDMvHkRhRctMub6670jSO3tqqvsa7ZsMebyy836tt3NIXwQ82XvvJOJn0Lytm41pl8/7++pTRtjPvooxQutW2d/GRMnmkMGr3S9Vu+CMlMjBcYsXpyG7y4/aQDXAB4aw4e7B57TToso+OijxojEF7zBln39dWP69697bjPF5iA+8nzZbrsZU1GRsR9Hwm65xfvHUFxszOTJabhQxM9xAgd6XvNLhhozdWoaLpqfYgVwnYmpssLixfDFF+7Hf/Mb54tHH7Wzb0wC/Q3G2MVQ5s2re6qErbzMWPow3/Vl335r25PDYPFiuP127zJPPw2jRqXhYp071315IJ/Qj3muRV9mLPz4YxouqqLRAK6yglfHZefOsO++2PFrV16Ztmt2ZB3PcwrNqXItc+edsHZt2i7pm5tusotTubn4YjvJJi7r1sGLL9rZPy+9BAsXNjy+ww51XwowlpddT/UKP9MA7ie31Fxv2oQSpJ//3P1j+G9+4xR6+OHYzSVJ3G7mWs8iV1+d0R9NTAsWGFNY6F7/3Xazzf8xVVQYc8EFtsMh2okOOsiY0aObPP8Fe3r+/NbeeJ/fP4KcFRFbosYezcBVVvBqPjn+eOeLN9/05dqX8Wd2Y4Hr8fvvt0lptrrjDjuK0s0DD8QxJHLzZth/f7ter9viMx9/DO+80+TpPfmSbvzgeuovZ+uC7H7RAK4ybs0aWLTI/fiIEdhkbsoUX65fRCV3cbnr8U2b4IknfLl0ylatssvCujn6aBg9Oo4T3Xab97uoBwFG8D/X41981z6p86rYNICrjJs2zf1Yr17QqRN2VoiPi6OM4VWGMt31+IMPeme5mfLYY97LuN54YxwnWbrULsmYguG4B/8vfuiW0rmVOw3gKuO8Er/hw50vPvvM1zoIcD03ux5ftCixdUWCsG0bPPyw+/HRo+2E1JiefdYueJICzwC+bpeUzq3caQBXGZcNARzgJ4ynJ2Wux596aIvvdUjEBx/A99+7H//d7+I4iTF2fGGKvAJ4WdVOga4smU80gKvMGTsWfvpTZk1c71pk+HBsp9pbb/lenUJquICHXI+//nYh6+96NLEx6D565hn3Y3123soRR0Q8sXWrbYaaOBH+8x+7D9pll9nlB+fMSbkuHVnHLnznenzWzOz4meUaXcwqDXQxqyRUVED79myvrKaICrYRfWfcVaug07R34+yJS90aOtKV5VTTIurxxziHc37TzLZdZHDXgk2b7HKvW1w+FNzd437+0O8Nu7busmWBDKMZy0u8QvTNMR9/sIKzLijyvQ65JtZiVrorvcqM6dOhspJl7OwavNtIOaUXnwefTrSLfo8YYWf1dO5sN2N84IG0V6uUtRzDW7xWt/teQ89yGuf8/VDYffeM7lrw+uvuwbuA7Zy65A5YsiKxk+6zD5x7rr0lYRcWuh4rW6HB2w/ahKIy49tvASijl2uRXmYR8u/n7CiJrVvhqafgqqtsj+Lf/uZb1U7HvW1iIgewpkXXzE7PNIZX73Efd3k479OVBIM32B0wJk9Oulq9PPoPytwPqRRoAFeZ4QRAzwAeGRAOOAD+9S/o189ONvGxueo43qAtG6Ie204z3rrzK7juOt+uH0vl3Q/w9tRS1+On7rs4+ZM/+WTSL9UAHjxtQlGZsX494B3AexORZT7zjHevnZtDDoHLL4eVK+1txYqmXzcaIlFEJUfzNv/hlKinHD+plP/7XeJVSYvPP+fjK9+mnIujHi5kG8df0B2ST6QTJwLt2tG7pAqWRS+iAdwfGsBVZjgBfBG9XYt4ZXRxadHCNrt4bc5YU2M3fmzkp7zmGsDfeXUrlRVFtCzKQCfm448zvuZY18MH7jifjoO6BlefCy+0aw0UFNBzE9AmerHvv7dDzVtE7xtWSdImFJUZcWTgCQfwggI48MD6xxdeGHtn3TvvjPr00T3n0qww+pogm7YV89mUDARvY2D8eN7lKNciPzm6CroFOPPxwQehSxfo1YvWIwbRqVn00S7G2K4MlV4awFVmOMPa0hrAL74YJkywo1NKS22Hp5fvv7frsEZq3hwefJD2X03koP4rXV/6/vuJVS0t1q1j8YoWfEsf1yLHX7Krs/ZAkk46yQbls86CIUOgWcSH9EcesW+Sja1aZRcknzOHXtu+dT21NqOknwZwlRnr17ONQpbS3bVIgwB+xx3QsaP7+c4+2+5oIAK//a0NKBHrVkd1991NFxLp0MFujtyzJ0fOvsf1pR984H1qXyxezAcc5np41+Jl7Dq0jQ2ybVzaMmIZNw569oTHH4cZM6C8HP73P9tx/LOfxczutSMzWBrAVWasX89yurqOAW/LBtqzvv6JQw+1wwdvvhkGDbKBtl07O5vzpZfgH/9ouGZqq1ax63DLLfZ8bdvWP/fjj/Dcc7BuHYfhHqU//xw2RB+o4p+yMt7ncNfDh++51q64dcIJNvAmK3Ir+aIiOz78/PPt+PvNmz1fqgE8WBrAVWasW8ePdHY93J2lNGhlbt/eBtrrroOvv7bDENets8F7bPTZfzG1aWPPt2iRbW4pKWlweCgz6MiaqC+tqbGtNb77+GM7BX77dsyiMs8M/PCtr9smj5fdd8iJy+67ux9zmz3k6I57Q/eqVclWSLnRAK4yY/16VuPeVtuJRqsftW/ftFC6prJ37GibXxYuhEsuqRsqUUgNh/CR68s+/TQ9l3e1fTsceSTsthuUlLDgDw/zIztGLSrUcMiXadjAc+zY6O3ctfXxWruWKL+3CLqgVfppAFeZsWgRa+75l+vhTru2g9NOg2OPhf32s80lfttxR7sP5Lff2unkhYUcyoeuxSdN8rk+ZWX1y7xWVTGJ/VyL7sEsSmk0O/TQQ+PYiqcRr08zMbJv8A7ga2o/zNTUxHwjUPHRAK4yo2NHVhdEzyYBOh0+zE7ceeMNm+q2DHBbru7d7djmm25iP9yj9LRpdk0u33zzTYOHXgF8f6J8HLjsMpg/P/7r7bornHyy+/EY7d8QIwOf/I39tNOsWewRQiouGsBVxnh9pE5lJFxKpk+Hiy6yoy2uvZbBfO06rb6qKuldyOKTQACP+kbTs6ftkCwvb9gxGU2LFvbTRzOPuX0pZuCrq9rafgtjsnuT0RDRAK4yJusC+LJldvPLTz6xQa9NGwpH7M3Iwe6Zp6/NKBEBfA0dmccA16L7MQlOPdUuRvXcc7ZNv1cve7B1a/sp5sgjo7+4Qwf473/huOO865NqBk4n6lawyeRiYDlEp9KrjMm6AN6tW/0StVVVdlKPCPvdDO99Hf0lvm4UFBHApzDStdjOLKUHS+Hgg2HUKHtrrGdPePdd+PBDePNNO1zSGDj+eDjmmPjGjceRgZewlWK2sJWSJse20ZyNtKUdGzWAp4kGcJUxWRfAI0Us2rGfe8tFYE0oXzDctVhd80m/frHPeeih9pYMY+yb3JYtNhuvro5arBOrWUr0JQxW00kDeBppE4rKmKwO4BGGu8dOfvgBlk92X5s7aVu2wPLldQ+9Avg+fG6/iCeAp2LkSPsNr1tnP6FUVdnZTMuWwXvv1RWL1YwCaBt4mmgAVxkTlgDerh3067rR9fgXh10Br76a3otG/HAMMJW9XYvuzVQ7yamz+8QoXzRvbq/btSvsXV+/uAL42rVZs7domGkAVxlhjHcAL3XfryB4r73G8OWvux7+omKQXSckmfXK3TirNQL8wE6spEvUYkINw5huVwTM4B6dtG1bNwGo1GX2KkQE8MpKu8uSSokGcJURmza5NqHSooUdOJEVamrg8ssZzlTXInXZ8e23u39TiYpYaMWr+WQAc2nN5ugzVYNUUFBXB8/JPES8M2szSso0gKuMiNV8kslksoF334X5820zhYsvGI7Zdz+7bknz6ItzJSwiA/cK4MNxelEzHcChbrXIuJpQQDsy00BHoaiMCEv7N++8A9iFrQrYTg1Nd+9ZRWdWPvNfuuyQ4LR1L4cdZkehrF/PjIt6U9tP2djwgVuhw37eC1AFRQN44DSAq4zI+gBeVWWz7/vvB6AVW+jLfNfJNF8NO4Mu619I3/VLSqBvXwBmuuwzCTDs7+fB/uel77qpSDSAR3zKUMnRJhSVEVkfwKur4Sc/afDU7nzlWnzWhh52/81F6R1SuGaN3TjIzR57pPVyqfnFL+Daa+l09hjXIquGHmk/WSxdCke5bw2n4qMZuMoIr/4rr413AlPSdCbhHsziBU6KWnwWe8Cvzkxvs8APPzDzxk+AX0Q9vMsuDfeiyLgzzgCgdAbwePQi67e1rvtkoVKnGbjKCK8NY7IiKEXpRd2DWa7Fv2J3+87ToUN6rv/SS7DLLsz4h3vn6dCh6blUunnNyk9loyDVlAZwlRFe/8jJbueYdn0abh7s1YQym0FsG+oxZTMR48bZzYWrqpjJENdiQ3bKzh0SNIAHRwO4yohQBPBGs4l6spg2RJ+RWUVL5h/3+9SvWV1t1/GuqQHwDOBDP3809ev5QAN4cDSAq4zYtMn9WNYE8L0bTl8vwHhm4V+VuK8YGLcXX4QlSwDYTgHz6O9adI8vnrCrCmaZoiIobDraErDvT7oZT/poAFfBuvZauPVWyucscS2SNQH8sKYbCHuORFnSPvVrvl4/ZX8hu1BJUdRirSmn5/bv7FDHLCOiWXhQdBSKCtbDD8PatZQzAlyWHG3z35eheYEdZjFokHs657fjj7dLr35Yvy+mZ0eme2yPjzF2NqdjDgNdiw5kDgIwe3aKF/VHmzbuw7zLy7NkqGgO0AxcBaempm78YDnuKVqbx/5iF4caMiSzn7cLCuDJJxsMe/MK4LPcD8Xnu+/s0qyOWAEcsLsHZSGvtWw0A08fDeAqOBs21C0h6hnAcf7Di4qijscOVI8e8OWX8NvfQocODMZlax5g8eIGa1AlLiL7BjuyxU1dAPea5ZNB2oQSDA3gKjgRk1ziCuBZMaMHaNXKbrW2ahXtv/yIHl2qXIt+7R7fY2uUTXtl4IOYDTvskNiu8wHSAB4MDeAqOGEN4LUKC2HYMPYY3sK1yMyZKZx/2rS6L7dTwFyPTYwHMgcuvth+SslCGsCDoQFcBccJ4IaQBnCH18J/SfcpGgNlZXUPy+hFBdFXNyxhMz1YAgcdlOTF/KcBPBgawFVwnABeQRHbXQZANaeKljhNFFkawAcPdj82Z06SJ123zm4UXHueGB2YBUKTcerZRAN4MHQYoQqOE8Djyr4hawP4IPe+xeQDeGEh/OUvdhLPkiXM+fwQ+CF60YHMsVuoZWnzCWgAD4oGcBWcHAngffvaEYbObPcGfvwRVr82iU7tqm2k32GH+E7arh1cemndw9lnAE9HLzpw79Yw8sTEKx4gDeDB0CYUFZwcCeDFxXaOkZs5Y66CQw6xu8Tvt19SA8S9MvlB159Qt9FEtvIK4F7LKKjEaABXwcmRAA7ezSgNxm9Pngx77gn/+U/c566pgblz3Y8PdG8ezxqagQdDA7gKTr9+cOCBlPd0H8YRlgDuFUSbdEBu3w7nnmtnWsZh8WLYsiX6seJi6NUrvjpmkgbwYGgAV8G5+mr4+GPK73zItUjrEYPhscfgzjuzd8cCEsjAa5WXw623xnVur+aTAQNs+3u28wzgG7bbveKWLrXLE6qkaSemCpznWuD9d4JzzgmuMomqqoIHH2Tg2yuAO6MWcR0COGFCXJfwGkue9c0nF10E335Lm+W9gehv1OUffQGdnKV3582zn8xUUjSAq8CFYjMHN82bw6230n/tFoQ7MFE+xK6kC2voSCmN9scsK7O7OcdYis+zA9Mj888KH38MX31FG9wHyzfoA4kY+64SF4IPYyrXhDqAi8DIkRRTwS4sdC3mmoW7rbEa+VqPAJ71GXirVkCjvoxGGgRwt8Z+FRcN4CpwodiNx8tI+/G/bkXAKFwDeIwxdDU1IQ/gzuqRGsCDoQFcBS7UGTjA4YcDzoqALqJ2ZN53X8yO2aVL3VsVioqgd+94K5khTgBvjfsbVTltMLUPNICnRAO4ClzoA/jIkbDXXoll4MXFcMYZMU/tlX3375+5zYni5jShtKSK5kRfdreGQrbWLtSlbeAp0QCuAufViuC1k0vWEIGrr04sAz/pJGjfPuapQz0CBRpswOHVjLIJ5xetGXhKNICrwG3d6n7MSeCy39ix9D9xd4QoC6IAK+jKWjrYB82bwxVXxHXaUI9AgQYBvAT34FyXgWsAT4kGcBU4r//Z4uhLYGelkkfvpXcLlyUDoX5DhiuvtDNw4uC1o08oMvCId+Bi3N+pt+AEem1CSYkGcBU4rww801tgJqRDBwYevKPr4dmyO9x2G9x4Y1ynizUCRTNw1ZgGcBW4XMnAAQYNc99ebc4vbrHLB8Q5933JEu8RKF4rIGaNiACuGbj/dCamClzOZOB4N2vM/jHOtcAdXs0nAwaEYAQKxJ+Bt2gPrTtCy5YBVCp3aQBXgculDNxzVcIEd+fxGoESiuYTgOOPtzteFBdTfNVg+DR6sS3j3oCfBlu1XKQBXAUulzJwr77JZcvszHnX0YNbt9pV+bp1g4ICzww8NAG8Rw97A0q6uhfz+htQ8dM2cBW4XMrAW7XyXp/bMwufPBm6d7cnGTiQ2eO/dS3qtZFytvL6XWrfZXpoAFeBqq62+xtEU1hoh0yHTdKbHNdu8FBRwfa53zB3407u17j5ZLjnnuQqmCFen6Y0A08PDeAqULGyb5Hg6pIunh2ZHu3akTv0LGQXKoiespawmZ5TX4h7R59s4RXANQNPDw3gKlC51P5dK+kMPKLRO+riV7XnZzYFmJCMI6zn1YSiGXh6aCemCsabb8LGjWwp3wE4PGqR4mIDhC8FTyoDNwamTKkvFyOAA7DrrknULnM0A/efBnAVjBtugGnT2MpAcFkEqmTxXGi9j+3Ue+cdGDYs2DomyWskyg8/wIYN0K5dowMLFsDa+h17vvbYwaYugIdiLn09zcD9p00oKhjOjLu6GXhRFLPVlvvxx1D1ZrZuDT17uh+fM/BEOPtsmDat/smI7Bu8M/DBfA2lpbDbbqlWNVCagftPA7gKhrOG7FaXjjpoNHMvNMsSWp4Tepa1gyeegL33httvt4ueTJpUd7yaZsyjv+vrBzHbrkEesh5ezcD9pwFcBSPeDLxWyAK4V0dmXfOIMXDNNXDyyfDss3XHF9CHaqKvqdKWDezM9zBqVDqrGwjNwP2nbeAqGE4Az9UM3CuAT6dRW/6LL3ofjzwvs223rrMPZ5hoBu4/DeDKf9XVUGW314orAxcJ15TMq65i6IT1wMNRD09nGDWIHQoYxZfs6XrqocywP4+99069ngHTDNx/2oSi/BexZGhcGXhJSdxLsGaFqVMZOOVx1z0gN9KORbjvRjy9hXt2vSdf2uaTtm1TrmbQNAP3X9IZuIg0A/YGDgQGAZ2A2vUzVzm32cBEYKoxZltqVVWhFRHA48rAQ9Z8Qps2tKCa3fmKL9krapHpDGNXFjZ53rQsYnrRKFxiP8OYDmeel87aBkYzcP8lHMBF5DDgHOAnQFHkoUZFIz8vVojIeOBxY8z7CddShVuiGXjYAninToANtm4B/Ev25EReavJ82enXsv7x6J82mlHN4O4b49rNPhtpBu6/uD6nikiBiJwrIt8B7wEnA8XYoL0e+BJ4Hxjn3N53nlvvlCl2XvOuiHznnCtEn5FVShLNwEOxNX0EZ4TInnzpWiRqO3erVkzf77eurxnUaSUtb70utJseaAbuv5gZuIiMBW4D+mKDcTnwCvAOtmnEc4UdEdkV29RyJDAW6A08AvxeRK4xxryS0negsp8zBhxyNAM/9FAoLGTY9umuRaayN00WCrjkEv43r/EUzXrDjts5tNk3aAYehHiy4BeBfsDHwIlAZ2PMmcaY52MFbwBjzHdO2bOAHZ1zfAz0d86tcl1pKfzyl3DiiWzp4T7vPLRt4L16wbXXMoSZFBK9q2ctpcynb/0T/fvDlVfy2Wfup90remtMaGgG7r94Avh7wAHGmEONMS8bYyqTvZgxptI5x6HAAcB/kz2XCpGBA+Gpp+CFF9hy2PGuxUoe+avdwmbcuMCqljbXXkvJicfaYX8uJrOv/aJtW3j5ZaqL2jB1qvsp9903vVUMWssWBpHoQyerq2Hbux/A66/DihUB1yx3xAzgxpjRxphJscolyhgzyRgzOt3nVdnN66NzcetmdtWnjh2Dq1C6NGsG48YxarR7k8hk9oVf/QrmzYMBA5gxAyoqopctKYE99vCnqr47/XRo1Qpp3oxi47Gx8egx8JOfNFkXRsUvqY5EEVkuIu+KSHhWHFJZweujc1jXA68jwr5n9HE9/NmAs+yaKF3tZpFezSf77GPfE0Kputr+omtqPHemr+vQ1gbxpCU7EmRH7KLOGsBVQjwz8BBNvnTjtWTJ7LmFrFlT/3jiRPeyoW4+KaofXdxgfZtG6jq0NYAnLZChfCLSUkSeEJGLgrieyl45nYFjl5Xt0sX9+PvOLIht2+CDD9zLhXDtqnoR78Sagfsr3nHghSlepxlwJhCuXVlV2uV6Bi4CBx/sfvydd+z91Kmwbl30MoWFsP/+aa9acCJ+kZqB+yveDHyTiHwhIo+JyAURz0fvYm5KGt2rPJXrGTjAUUe5H3vvPbuqbG0gj2bECGjfPu3VCk6iGbhbT66KKd5ukpbAntBk3csFIjIDmA7MAGa4jA3v7tzrbyrP5XoGDt4BfNkymDUL3n7bvczosI/N0gw8MPEG8JOAvZzbnkDtOK9uzu3o2oIisgmYhQ3os4CNQO1qPAtSrrEKtXzIwLt2hSFDYObM6Mevvx7P8d+5FMC1DdxfcQVwY8yLRMyaFJEa58urgQHAEOe+OdAG2A9o3I9ugH+mWF8VcvmQgYMNwm4BfPx499eVlsKe7suDh4OOQglMqiNN7zfGjtR3xoQPBIY6tyHAYOwysyuxKxFqJ2ae82ruzKUAfsopcOedib/upJNsJ2aoxdmEUlG7mKkG8KQlG8DbY4Nz3U/eGFMNzHRummmrJrZts7doREK1EX1MQ4fCsGEw3X19q6jOOsuX6gQrIoAXeXR7aQaeuqTGgRtjNhpjJhtj4h2FolTM7Dtkm67HlGgw3n338C9gBTTMwMV96aSKXQfD0UfbdzqVFF2TWwXGK9EqKnI/FlannZbY0ubnnZcjb2LHHgsbNkBVFUVXXOJarOKsC+Ctt+DKKwOsXG7RAK4C45WB52IA79ABbrwxvrKDB8O55/paneC0aGFXXGze3PP3qi0nqdMArgKTLx2YkS65xLaHx/L3v+dWH0Atr9+rzt9JnQZwFZh8a0IBu6Lg+PHQr1/044WF8M9/hnzxKg+agftLA7gKTD5m4ADdu8OkSXDxxQ03G9p/f5gwIdS7psWkGbi/NICrwORjBl6rtBTuu88uYDV1KqxaZZeTDfWiVXHw+r1qAE9dWJeMV2Fy111QWUlFWT/sqgxNFW9ZAxPn2B3Yhw61HWE5qHlzGD4807UIjjah+CuQAC4iTzhfTsHOyNwexHWj1GNn4GZgNFAKLAdeBW4yxrgs7qlSdtddsGYNW/kpbgG86MtJcOBP7YOVK6Fz5+Dqp3yjTSj+CqoJ5Uzgl8DDwFwROTWg69YRkV2BacCvgM+xa5MvBC4BPhOR0qDrlDcq7WSOuqnTUTSYsdeypd81UgHRDNxfQbaBi3PbDXhaRFyW+vHNQ0Bn4GJjzBhjzJXGmEOxgbwfcFvA9ckfcQTwBmtmaADPGZqB+yuQAG6MKTDGFGDXBT8LeB4bTAPhZN9HAmXAg40O3wBsBv5PRFqh0qumxm5yS8TaF1E0yMBztP07H2knpr8CHYVijPnBGPOUMeY0Y0zXAC99iHP/njGmJvKAMaYcmASUACMDrFN+qKxfCyOuDLx5cyjQwVG5QptQ/JUv/ym10yjmuxyv3Wiib+MDImJi3Xypca6ICOBxZeDafJJTtAnFX0kFcBEJ25pp7Zz7DS7Ha59v739V8kyiGbgG8JyiGbi/ks3APxIRj53/cocxRmLdMl3HrJZoBp7rM3ryjGbg/ko2gLcGxovIr9JZGR/VZtjtXI7XPr/e/6rkmTgzcG1CyU3aiemvZAP4i9j9L/8hItfF8wIROVpEvkjyeqn6xrlv0sbt6OPcu7WRq2RpE0pe8/p1VlbaQUoqecnuyHMSdvy0ADeKyKMiEvVcIrKfiHwMvAFkauuNj5z7IxvXU0RqN2Hegp0pqtJJOzHzWkFB7CCukpf0KBRjzB+Ai7G7zZ8NvCoidf+hIjJURN4CPgH2xwb7ualVN+m6fge8B/QCLmx0+CagFfC0MWZzwFXLfV26wM03wzXXUNFnD9dixYN2tWuqDhkSYOVUELQj0z+S6raWIjIGeBYoAqYCf8BOTx9L/ezL+dg1SP6dqX00nck8k7ETiF7DvpmMwI4Rnw/sa4xZk+S5DYBuEert2GPtDlrRjB8Pxx8fbH2Uj8rL7ZKLmzfT9ZB+rFgTfXLWD5fcRbfClXDoofYPRDUgzh57boMlUl7MyhjzqogcArwO7I3NuMEG7m+BW4BnG0+gCZox5jsRGU79YlbHYBezug9dzCoQ+boeeF564AG45hoAilgI9I5arOK+R4BFducLDeAJSzmAi0gn4ERsMwTYwG2Aa4E7M7XyYDTGmKXYxaxUBuTzeuB5J2LnigbLJDRS1y+yWVsvk5F0G7iIlIrIn4BF2GaTEmAd8CM2iP8a91EfKg/l26bGeS0igDdYqKyRupFJGsCTkuxMzNuxgfsybOa9GdtU0hu7nsh8oAcwSUQOTE9VVdhpE0oeKSmp+1IzcP8km4FfiZ3MUwXcC+xijLnBGFNujCkDRgGfYaemvysip6ReVRV22oSSRxLNwLds8btGOSnZAL4deAzoY4y51BizOvKg0yF4KPAK0BJ4RkQuT6mmKvQ0A88jcbaBaxNKapIN4AOMMb8xxnzvVsAYU4nt3HzQuc4dItJ4LW6VRzQDzyPaiRmIZGdifhtnOWOMuQjb5AJwXjLXU7lBM/A8EtEGrk0o/glqR567gNOB6iCup7JPTY33tGmdQZ9jNAMPRCC70gMYY/4tIsuDup7KLl7Bu0UL3YQn5+gwwkAEvaXahCCvp7KHNp/kmUSHEWoTSlJiZuAiMg242RjzWjovLCI/A641xoRtdx+VBO3AzDOtWkGPHtCqFUXrO9pFK6KoOGg0HFMKrVsHW78cEU8TyjDgZRGZBTwEvJjsuiEi0hE4CTgfGJzMOVQ4aQaeZ5o1g8WLASi+HbgmerGKUYfA5YdEP6hiiqcJ5SDgc2AI8AiwXETGi8iFIjJCRFy7n0SkSERGicjFIvIm9n34QWB34H/OuVUe0Aw8f+lysv6JmYEbYyYCo0TkBOBqbEZ+HFC7dNg2p3NynXMD6Ah0ALoChc5ztcshTgNuN8a8kpbvQIWCZuD5y+v3qwE8NXGPQjHGvAS8JCLDgHOBnwE7YrdW6+Hc3PwIvAQ8ZoyZkXRtVWhpBp6/dF9M/yQ8jNAYMx24ALhARPoABwADgR2ATk6x1cAqYA4w0RizID3VVWHlNcggYsCCykFev18dfJKalMaBO4FZg7OKySsD1yaU3KZNKP4JbCKPylPnnANlZWxdeyQQfT2z4sXz4P737H/62LFQWhpsHZWvNID7J6kALiL7AF8ZY/THr7x99hnMmcNWursWKflqClxyiX0wcqQG8ByjTSj+STYDnwJsF5HvgJmRN68VClUeclKsLbj/FzeYaq09mjlHM3D/pNKEUojdMq0PdtlYAERkPY2COjDbGFOVwrVUWDnDDOqmTEfRIIBrg3jO0QDun2QDeG/sePDI207OsQ7AwTScpLNdRL7BZuinJ3lNFUbOf2jcAVwz8JzjFcC1CSU1SQVwY8xiYDHwau1zzu70tcH8YOAw7Bjx2usMwg431ACeT+JoQikh4r9YM/Cc49UGrhl4atK2GqExZrUx5r/GmLuMMcdgJ/nc6xyeAdwOvJGu66kQMKZuHVnNwPOXNqH4x7flZI0x640xlwKnYNdR2WKM+alf11NZKGKaXVwBvHlzKCx0LafCKVYANya4uuQa39cDN8aMA54CbhARXYEwn0SkV14BvK4JRZtPclKzZva9OZqID2kqCUFt6PAUtj38ooCup7JBRAYe1zBCbT7JWdqM4o+gAniZc394QNdT2SDODLwugGsGnrM0gPsj2ZmYzwLTsZ2T040xa2K8pHYaXrdkrqdCSptQlENnY/oj2XHgv8B2TgIgIsuICOjADGPMIudYW+Bmp2hSO/mokOrdGz7+GCoq2HLRUJgfvVjxKWOgY3/YYYcga6cCpBm4P5IN4OOBodSvAb6Tc6vd5AER2YxdUrYb0AIwzutUvmjVCg48EICtHs3bxZdfZGcPqNyyYgW8/TZs3UrxxrFAl6jFtp73e2g5EwYOhL/9Ldg6hlyyE3nGAIhIB2wgHxZx3885b2vnVutL4Mqka6pCTZeTzUPffgtnnQVAMbvjFsC3TJ4OfKypeBJSXQ98HfCRcwPA2SNzMHbm5c7O0zOBt40xNalcT4WX1/+mbuiQoyJ+sQ1m2zZS1z+iATxhaV8P3BhTid33clq6z63Cy6ujSjPwHBXxi20w27aRugCuvZkJC2oYocpz2oSSh+IM4HVzBDQDT5gGcOU7YzSA56VEm1A0A0+YBnDlO6+dx1u00OVPclaiTSiagSdMA7jynWbfeSqZJhRd2SohGsCV73QESp6KWMUqriYU8P64pprQAK58pyNQ8pjzDh1XEwpoM0qCNIAr32kTSh5zfsFxNaGAdmQmSAO48p0G8DwWRwDXDDx5aZ/Io1Rj2gaex04+Gdato6Rsf3g3epGtBx4F1/3XBvudd45eSEWlAVz5TtvA89gddwBQ/BKuAXxLh53hcA3cydAmFOU7bUJRupysPzSAK99pE4ry+j1rAE+eBnDlO21CUZqB+0MDuPKdNqEor9+zjhxMngZw5TsN4Er3xPSHBnDln912gzZt2HTLX12LtH7j33DaaXDOObAm1t7YKqxatXI/tnlzcPXINTqMUPmnvBw2bWKTR57Qeu5UmPucfXDLLQFVTAWtdWv3Y5s2BVePXKMZuPKP03ZSThvXIm0or39Q5LHzsQo1rwx861bYti24uuQSDeDKH8uX2wwc7wDemoj0SxvEc1ZBgXcQ1yw8ORrAlT8++6zuy024f36uy8DbtdMMPMe1cX8f1wCeJA3gyh+TJ9d9GVcGPmSI3zVSGebVDl5e7n5MudMArvyRaAa+775+10hlmGbg6acBXKVfZSV88UXdw7gy8FGj/K6VyjDNwNNPA7hKvwULoKqq7mFcGfjIkX7XSmWYZuDppwFcpd/KlQ0exszAd9sNOnf2u1YqwzQDTz8N4Cr9ItKp7RSwBffxY63YDH37BlErlWGagaefzsRU6deyJQwcCJs3s3kjsC56sRI2U0iN93+2yhlev2bNwJOjAVyl3+jR9gZsWgbsFL1Ym47N4ek3YYcdgqubyhjP6fTlBjZthg0boFMnmwSomDSAK195ZVatO7SAY44JrjIqM554AubNo80nI4GxUYuU33wP3PwH+2DKFBgxIvXrlpfDCy/AjBm2X2bwYDjkENh//9TPnSU0gCtfebVtastJnvjXv+Djj2nN+bgF8E2R/SQbNsR/7kWL7EIqO+xgZ/OK2OfHj4cLL4Tvv68vO26cvT/5ZLjvPthxx8S+jyykAVzZTGXFCujVC5o3T/up3Xh9pFY5pF07oNHCZY00GKm0caP3+WpqbAB+6imYNav++X32gdtuA2NgzBh7H81//mMD/8SJ0KJFfN9DltJRKPnKGPj3v2H33e0/WN++drWhAw+0H2HTRDNwRdu2QKOFyxppMFfAKwOvrLTrx196acPgDfD553DEEXDkke7BO7Ls9dfHqnnW0wCej7ZtgxNOgFNPha+/rv9jr662WcmoUfCnP6XlUpqBq7Rm4FdcAc8/n556/etfsQN9ltMAno9uvhleecW7zFVXwauvpnwpzcBVbQBPOQP/8Ud49NH01Wv5cvjqq/SdLwM0gOeb5cvh1lvjK3vVVSlnKJqBq9omlJQz8CefTP8W9jNmpPd8AdMAnm/eeSf+oDxvXn2GsmEDTJgA//gHfPABrF4d1yk0A1cJZ+Dz5kUv9OGH6ayV9emn6T9ngHQUSr6JWKc7Lh9+CC+/bNvEKyvrn2/WDH73O7juuroMKxrNwBUdOgAJZOAffmj/cCLf4aur4b33/KphaGkADytj4IcfbLay8852Qahmcfw633orsev8/vfRn9+2De6+22bjn34KJSVRi3kFcM3A88TAgUDsDNwAAjZRePdd2L4dnnvOJghz5/pTt9tu8+e8AdEAHjbG2M7FSy+FsrL651u1sm3Wf/yj9zTkZcvSW5/p0+GCC+yY3Ci8mlA0A88TAwdCp060WL2aFlRSRdO/zxoK2UoxJbt2s2O4Bw60w1zHj/evXp06pbaMQ3U1fPwxTJoEq1bBLrvAwQfDnnumrYqxaADPVqtWwfvv2/GqLVpAv35w/PG2A/L++5uW37wZrr3WZixvvmkn5YCd9PDxx3YqsV/rSzz9NNx1V9QlYTUDVxQW2r/dJ5+kDeWsiRLAAco/mkbJQf3rZ1PGmtCTqlRmYn71FfzylzaBaezkk+Hee6FLl+TPHycN4NnGGNvbfumliU0prjVnjh3H/fnntrnkT39qmKn7oaYGXnsNzj23ySFtA1eAzaqffJLWbGINnaIWKd95ADtKxBN+B/BkA+ycOXbWZ0VF9OP/+Y+9nXkmnHIKHHVU0lWMRUehZJu774azz04ueNdasQJ69IDzzvM/eNdyGY61dq37S9q396UmKhsdcQScfDLte7h3eDf5W/E7gE+fDl9+aZsVq6vje011tZ0J6ha8Iz31lF2V85hj7Bh2H2gAzybTpsHVV2e6Fsl56KGoT69Z4/6S0lKf6qKyT3ExPP88pbt1dC3S5G/F7wC+di3stRfstJNtpuzUCQYNgqVL3V8zeXLiY8ffftvOfI7YZjBdNIBnkyeesKM7gjZ0qP04WZDin8OHH8I998CvfmXb8PEeLt4p+idplcO8fudN/lb8DuCNrVljm0fcOme2bYOzzkru3J9+Cn/+c/J1c6Ft4EEZOdIOtevWLfqta1c7dCpop54Kzz5rv962zQbe5cttZpKoww6r//r009ky6jDXT5rNmnkOH1c5yutTV5MMPBPb9LRoUTfxqIHPPoNf/xoWLkz+3M88Yz9hi8QuGycN4EGorIT//S/TtYgushe9WTP7RtK1qw3mzZsnP5V+6VLWeGx12bFjWv+OVUjEHcAXLvRv7LeXHXds+of57LP2U2W87eRu5s2zy9jusktq54mgTShBWL480zVoKPJz7Ny5dsJEY4WFdnJQsu6+mzUvTfCoQrhXgVPJ8QrgdU0oL70Ee+wRSH2aaDy08PXX4YwzUg/etaINO0yBBvAgpHvyTKoaNza6ddrsumvy15g9m9W/d180q3TORPtR9Ze/TP4aKnS82sDXrAEWLLB/E5s3J37yoUNtk+D119u5CVOmwAMPJHaOyKGFmzfbzLumJvG6uPnwQ5uFT59u1xZa57Ljd5y0CSUI2RbAG/v2W7s2ck2NHX7Yvbu979o1pdOuwT3dKmWN7aSKXF9F5byYTSiXXZZc8G7ZMnp2m+jaP5EZ+NNPew+jSsZDDzUcsfXee3aIZZI0gAch25pQGluwAB58MO1jVb0CeCecTwE6myeveAbw1QY+/yCxEx5wgE0E3LYCXLkysfNFBvCXXkrstclYvz6ll2sAD0K2Z+AXXODLaVe7zLgDJwMHu4aLyhuebeArqt0XzykpsWuM7L03DB9ub7vtFnvoazIBfMMG+4n0/fcTe20yUpmwhwbwYGR7APdJzCYU0Aw8z3i2ga8vbFhwt92gTx9736WLDe7r19u27XfegZ494ZZbvC+4YkViFZw0yS4/EdSnZs3AQ0ADeBN1TSiprAanQqdtWztaNdp8tYqqQrZQTAlbbUf76tXeG2yPHRv7golm4OPGJVY+VRrAQ0ADeBN1GfgBBwRUG5UNROwcALfuljWUUsL38Z0snqm8r75q//9WrLDB/I47YPHiuOvrO21CCYEgOzE7drQbPMyaFdw1XcRsA+/QwQ79UnmlUyf3AL6aTnRPZwDv0cPealVWwiWXxHf+IKSYges4cL9t3ZryWM+4bdhghz3NnGk7eTIsZhPKBRfYCUMqr3iORPH4m0noRG78WoBnhx2SW14zxQxcA7jfWra0S7pOnmyHJT3wgJ3ZlW5DhjRcXOS00+J/baJTe1u1iqvpw7MJpcWm7MqEVGBKi9zHeScUwJMJxn4E8FNOsR8p1q2znauJ0DbwLFdQYHvLe/asf27ZMrt7zd13p+86jYPwqafa3XtiTUQoKrJbV40aFd+Ms7Zt7YJXTzwBEye6FttKERuJsigQINTQYVR/7cDMU6XLZgGjoh5bSQK75GRDAC8osDM/a3V0Xy43Km1CCaFu3ezSkn/7W+Kv/d3v4LjjoH9/u3JarT59Gpbr3NlmA17D9Fq2tDvp7LMPXHFFfNe/+mp73Rgz3JbQw/VYF1ZQuH/0f2CV49avZ6f5E1wPL6an67EmkgnG6U4a7rwTBgyof5xoM4p2YoZYzwT+WAFGjLDrbdfavt3uTP/tt9GnvQ8fDp98YsfKvvpqw5UFjzgCrruuvink5pvtNmwfeMyE+/nP4fLL7dczZ3pWtYxersd6dtoMRx7p+XqVo0Toec4R8HD0w15/N00kE8DTuYvI00/D6ac3fC7aUrSRSkpskG/Xzt7vtFNKVdAAnknduydWvvGiU4WFTXvZGxs2DF5+2bbDl5XZAbi9ejVdabBZM7se+UMPwY03Ntzfascd7eSGM86oX2pzyRLPqnr9I/Y6vA8c2Mf1uMphBxxAr7ldgehr3/uegZeUJP6aaCZMgIMOavp8hw7w+OM2OEcG6nbt7M1tyn+SNIBnklcALy21x5s1s5l0rEAdS69e9TvVuykshIsuggsvtOsxL1wIffva60ZOWa6dEefB6x8xVjVUDquupte2Ba6H487Amzd33zknlp49UxsLfuaZ0YM32Holu2tPEjSAZ1KHDjZYdu1qx253725vO+2UvkwhGQUFNkN3Ww/8hx9insKzCSXBliOVQwYOZOd5r1PAdmpoOoR0DZ3YRCtaEzFS5fDDbb9R7ezM1attYpPsjiD9+6cWwLMoA9EAnkkiyXVkZtr3sSdaeDahuB9Sue7QQ2nx8svsxA8sdenoXkxPBjHHPvj6a7vRcDodfnhi2xeedVZ9E+SSJVn1B6yjUFTiNICrZP3859C5Mz1xz4Dr/naeey79wRvsENt4h/s995xt0/7gA/juO6iogF/8Iv11SpIGcJU4tx18HBW0ZDndXI+n0pSvQq5zZ/jXv+hFmWuRMnrZIOlXoOzWDf7xj9jlzj/fTtKJVFjYcPhuhmkAV4lr0wYGDnTdVn4p7p2znTtntnlfZYGjjqLX6e4zecv2/z/45z/9rcPPfmbX+46WTbRqBTfcAPffn/U7b2sbuErcJZfUT4PfuNF2ai5daptWvv+e+ZM7uI0S0+YTBUCvg3vBM9GPze84EtI72i66ww6DOXPg00/tOuBgZzSPHt1wb8wspgFcpaZtW3uLmI029UZcA3gq+ySr3OH1d/DFF8HVg1at4Kij7C2EtAlFpd3Uqe7HsmCRRJUFhg1zP7ZsWd4uoZ8wDeAqrYzxDuB77x1cXVT2atfODsd24/U3pOppAFdptWSJXawwmoICuy+tUuD9Zv7558HVI8w0gKu08sqcBg3STehVPa8Arhl4fDSAq7TymuCmzScqktffw6ef2iV3lDcN4CptqqrspkNu9tknuLqo7Dd0qPvifFu3whtvBFqdUNIArtLmv//13v7z6KODq4vKfkVFdii2m+efD64uYaUBXKXNgw+6H9tvP51Cr5pqPFM90ltv2RWNlTsN4Cot3n8f3n7b/bjXP6rKX2PG2J39oqmuhquuCrQ6oaMBXKWsrMxu1uOmoABOPDGw6qgQadcOjj3W/fi4cfDAA8HVJ2xyPoCLSHMRuUREnhSRGSJSJSJGRM7JdN3CbssWePRRO7ty+XL3cqeeGpqlJVQG/P733scvvthugvPdd4FUJ1TERG50m4NEpD1Q27W2EqgCugPnGmPiWFMyrmsYgFz8WRoD5eW2c/KHH2y2PXeu3ZR+yhQbxL20bAnz52v7t/L2s5/ZfbdjGTQI9t3XTgjr3dvu7lRaaje3apaDKzuJsxqiMSbqsog5+C03sQU4BphhjFkuIjcCN2SiIs8/bxfqMcb9VlPjfTzVW+PzV1dDZaW9VVTUf11ZaYPzunWwfXvy3/Mf/6jBW8V2552207Kqyrvc7Nn2Fk3btnal46KiprcWLexS3gUF9fe1t8jHjY/Vql1VNt33d96Z2htPzmfgjUUE8MAz8DPOgKefTscVw+GAA+zQQrdOKqUiPf44nJNnDZtVVd4b1cfKwHO+DTybZPna8Gk1YID9SKzBW8Xr7LPhuusyXYtw0QAeg9Ph6XmL/1x+1jR7jB4Nn3wS/7aDStW66Sa7z7dXVppLUo0JGsADlOsBvEsXePhhePNN6NQp07VRYSQCF15oF7M6/PBM1yb7hSKAi0hZPJlwxM1ls6bEGWMk1i3+7yNdtcoeRUX2H+3JJ+2sufPOa9j5o1Qyhgyx/SeffQa/+Y3dh1g1FZZRKN8BFQmUz8r9PMIawFu2tEO1Ona0I0p694Y+fWDkSLuzShZt0q1yzMiR9vbww7B4sd268uuvYdEie1u1CtauhQ0bMl3T5KQaE0IRwI0xHkvehMfPf253IRHxvhUUxC6Tyi3y/M2a2QBdVGTvI29FRdC+PRQXZ/onp/KdiN0Q221T7G3bbBDfssUOh629RQ6Prampv23f3vA+2te1g8r8uq/9vlIRigCeK0aPtjelVHo1a2Y/JZaWZromwdLWSqWUCqm8mMgjIlcCtVuoDgWGAJOBBc5zn6YyqSeXp9IrpTIn1kSefAngE4CDPIr80xhzZgrn1wCulEo7DeAB0ACulPKDTqVXSqkcpQFcKaVCSocRppGkOqhTKaUSoBm4UkqFlHZiZkhEx6em7XlGf/f5K92/e83AlVIqpDSAK6VUSGkAV0qpkNIArpRSIaUBXCmlQkoDuFJKhZQGcKWUCikN4EopFVI6kUcppUJKM3CllAopDeBKKRVSGsCVUiqkNIArpVRIaQBXSqmQ0gCeBUSkuYhcIiJPisgMEakSESMi52S6bip9RGRnEXlCRJaJSKWIlInIvSLSIdN1U/4QkRNF5AERmSgiG53/62fSdX7dkSc7tALudb5eCawAumesNirtRGRXYDLQGXgNmAfsA1wCjBaR/YwxazJYReWPa4EhwCbge6B/Ok+uGXh22AIcA3QzxnQBnshwfVT6PYQN3hcbY8YYY640xhwK3AP0A27LaO2UX34P9AXaAuen++QawLOAMabKGPO2MWZ5puui0s/Jvo8EyoAHGx2+AdgM/J+ItAq4aspnxpiPjDELjE8zJjWAK+W/Q5z794wxNZEHjDHlwCSgBBgZdMVUuGkAV8p//Zz7+S7HFzj3fQOoi8ohGsCV8l87536Dy/Ha59v7XxWVSzSAp4kzJMwkcEvbUCKlVH7SYYTp8x1QkUD5ZX5VRGWd2gy7ncvx2ufX+18VlUs0gKeJMeawTNdBZa1vnHu3Nu4+zr1bG7lSUWkTilL++8i5P1JEGvzPiUgbYD/sXIApQVdMhZsGcKV8Zoz5DngP6AVc2OjwTdiZuE8bYzYHXDUVcrojT5YQkSupn2Y7FDv9djL1Q8w+Ncb8IwNVU2kQZSr9XGAEdoz4fGBfnUqfe0RkDDDGedgFOApYCEx0nlttjPlj0ufXAJ4dRGQCcJBHkX8aY84MpjbKDyLSHbgZGA2UAsuBV4CbjDHrMlk35Q8RuRE729bNYmNMr6TPrwFcKaXCSdvAlVIqpDSAK6VUSGkAV0qpkNIArpRSIaUBXCmlQkoDuFJKhZQGcKWUCikN4EopFVIawJVSKqQ0gCulVEhpAFdKqZDSAK6UUiGlAVwpH4lIHxGpdvZB/ZNLGRGRcU6ZTSKyd9D1VOGkAVwpHxljFgBPOQ9/KyI7RCl2N/BzYBtwkjFmakDVUyGnAVwp/90MVGJ33rks8oCI/Ba41Hl4vjHmrYDrpkJMA7hSPjPGLAUedh5eKCKdoW63lvuc52/SHZdUonRDB6UC4DSdLARaA38BXsBudlwMPGGMOTuD1VMhpQFcqYCIyM3Addgd6DcDOwBvAT81xmzLZN1UOGkAVyogItIWWAR0dJ76AjhYd6NXydI2cKWC0xE70qTWpRq8VSo0gCsVABHpCLwDdI54+pIMVUflCA3gSvlMRIqA14F+wFJsOzjAWBHZM2MVU6GnbeBK+UhECrAjTsYCG4D9gXnAXGA34F1jzOjM1VCFmWbgSvnrPmzwrgJ+Zoz52hlxcotz/CgROSBjtVOhphm4Uj4RkcuBOwED/J8x5tmIY4XAHKAv8KkxRoO4Sphm4Er5QER+AdQuXnVNZPAGMMZsx06xB9hfRI4Jsn4qN2gGrlSaicgh2BEnLYC/G2POcylXAHwNDACmA3sZ/YdUCdAArpRSIaVNKEopFVIawJVSKqQ0gCulVEhpAFdKqZDSAK6UUiGlAVwppUJKA7hSSoWUBnCllAopDeBKKRVSGsCVUiqkNIArpVRI/T8ll8Cl3nUOOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "# Create the figure and axis objects with reduced width\n",
    "fig, ax = plt.subplots(figsize=(5, 5))  # You can adjust the width (7 inches) and height (5 inches) as needed\n",
    "\n",
    "# Make sure the font is Times Roman\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "\n",
    "final_time_output = prediction_tensor[-20, :]\n",
    "final_out = final_time_output.detach().numpy().reshape(-1, 1)\n",
    "final_true = u[-20, :].reshape(-1, 1)\n",
    "\n",
    "# Plot the data with red and blue lines, one with dotted and one with solid style\n",
    "ax.plot(x.T, final_out, color='red', linestyle='dotted', linewidth=12, label='Prediction')\n",
    "ax.plot(x.T, final_true, color='blue', linestyle='solid', linewidth=7, label='True')\n",
    "\n",
    "# Set the axis labels with bold font weight\n",
    "ax.set_xlabel(r\"${x}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "ax.set_ylabel(r\"${u(x, t)}$\", fontsize=26, color='black', fontdict={'weight': 'bold'})\n",
    "\n",
    "# Set the title with bold font weight\n",
    "ax.set_title(r\"${t = 0.81}$\", fontsize=26, color='black', fontweight='bold')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 3\n",
    "ax.set_xticks([-1, 0, 1])\n",
    "ax.set_yticks([-1, 0, 1])\n",
    "\n",
    "# Set tick labels fontweight to bold and increase font size\n",
    "ax.tick_params(axis='both', which='major', labelsize=20, width=2, length=10)\n",
    "\n",
    "# # Set the fontweight for tick labels to bold\n",
    "# for tick in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "#     tick.set_weight('bold')\n",
    "\n",
    "# Set the spines linewidth to bold\n",
    "ax.spines['top'].set_linewidth(2)\n",
    "ax.spines['right'].set_linewidth(2)\n",
    "ax.spines['bottom'].set_linewidth(2)\n",
    "ax.spines['left'].set_linewidth(2)\n",
    "\n",
    "# Set the legend\n",
    "# ax.legend()\n",
    "\n",
    "plt.savefig('LSTM_0.81_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7673683",
   "metadata": {},
   "source": [
    "### contour plot 80-20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f0a27cca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['Times New Roman'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator\n",
    "\n",
    "# Assuming you have defined concatenated_tensor as a PyTorch tensor\n",
    "# concatenated_tensor = torch.cat((tensor1, tensor2), dim=0)\n",
    "\n",
    "# Convert concatenated_tensor to a NumPy array\n",
    "concatenated_array = concatenated_tensor.numpy()\n",
    "\n",
    "# Define custom color levels\n",
    "x = np.linspace(-1, 1, concatenated_array.shape[1])  # Replace 0 and 1 with your actual x range\n",
    "t = np.linspace(0, 1, concatenated_array.shape[0])  # Replace 0 and 1 with your actual t range\n",
    "X, T = np.meshgrid(x, t1)\n",
    "\n",
    "# Define custom color levels using the minimum and maximum from the NumPy array\n",
    "c_levels = np.linspace(np.min(concatenated_array), np.max(concatenated_array), 400)\n",
    "\n",
    "# Plot the contour with interpolated data\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.pcolormesh(T, X, concatenated_array, shading='auto', cmap='coolwarm')\n",
    "\n",
    "# Set the fontweight for axis labels to regular (not bold)\n",
    "plt.xlabel(\"$t$\", fontsize=26)\n",
    "plt.ylabel(\"$x$\", fontsize=26)\n",
    "plt.title(\"$u(x, t)$\", fontsize=26)\n",
    "\n",
    "# Set tick labels fontweight to regular (not bold) and increase font size\n",
    "plt.tick_params(axis='both', which='major', labelsize=20, width=3, length=10)\n",
    "\n",
    "# Set the fontweight for tick labels to regular (not bold)\n",
    "for tick in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    tick.set_weight('normal')\n",
    "\n",
    "# Set the number of ticks for x-axis and y-axis to 5\n",
    "num_ticks = 5\n",
    "x_ticks = np.linspace(np.min(T), np.max(T), num_ticks)\n",
    "y_ticks = np.linspace(np.min(X), np.max(X), num_ticks)\n",
    "\n",
    "plt.gca().xaxis.set_major_locator(FixedLocator(x_ticks))\n",
    "plt.gca().yaxis.set_major_locator(FixedLocator(y_ticks))\n",
    "\n",
    "cbar1 = plt.colorbar()\n",
    "# Set the number of ticks for the color bar with uniformly distributed numbers\n",
    "num_ticks = 5\n",
    "c_ticks = np.linspace(np.min(concatenated_array), np.max(concatenated_array), num_ticks)\n",
    "cbar1.set_ticks(c_ticks)\n",
    "\n",
    "# Set the fontweight and fontsize for color bar tick labels\n",
    "for t in cbar1.ax.get_yticklabels():\n",
    "    t.set_weight('normal')\n",
    "    t.set_fontsize(26)  # Increase the font size for color bar tick labels\n",
    "\n",
    "# Increase the size of numbers on axis and color bar\n",
    "plt.xticks(fontsize=26)\n",
    "plt.yticks(fontsize=26)\n",
    "\n",
    "# Increase the tick size and width of the color bar\n",
    "cbar1.ax.tick_params(axis='both', which='major', labelsize=30, width=3,  length=10)\n",
    "\n",
    "# Add a dotted line at t = 0.8\n",
    "plt.axvline(x=0.8, color='black', linestyle='dotted', linewidth=5)\n",
    "\n",
    "#plt.savefig('Contour_LEM_20.pdf', dpi=500, bbox_inches=\"tight\")\n",
    "plt.savefig('contour_LSTM_20.jpeg', dpi=500, bbox_inches=\"tight\")\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc96e442",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch",
   "language": "python",
   "name": "pytorch"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
